No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A novel hybrid DEPS optimized fuzzy PI/PID controller for load frequency control of multi-area interconnected power systems
Abstract
In this paper, a novel hybrid Differential Evolution (DE) and Pattern Search (PS) optimized fuzzy PI/PID controller is proposed for Load Frequency Control (LFC) of multi-area power system. Initially a two-area non-reheat thermal system is considered and the optimum gains of the fuzzy PI/PID controller are optimized employing a hybrid DE and PS (hDEPS) optimization technique. The superiority of the proposed controller is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as DE, Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and conventional Ziegler Nichols (ZN) based PI controllers for the same interconnected power system. Furthermore, robustness analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Additionally, the proposed approach is further extended to multi-area multi-source power system with/without HVDC link and the gains of fuzzy PID controllers are optimized using hDEPS algorithm. The superiority of the proposed approach is shown by comparing the results with recently published DE optimized PID controller and conventional optimal output feedback controller for the same power systems. Finally, Reheat turbine, Generation Rate Constraint (GRC) and time delay are included in the system model to demonstrate the ability of the proposed approach to handle nonlinearity and physical constraints in the system model.
... The old proportional integral (PI), proportional integral derivative (PID) tuning based the LFC scheme [1] were applied to regulate frequency in the MAPS in the early days. However, with the complexity in modern power system, these controllers were seen degraded [1]. Recently, the development was made to use some algorithms combined with PID for the complex power system which were discussed in [2]-[9]. ...
... The job of frequency regulator otherwise known as the LFC is to retain the regulation frequency over randomly active power load changes sometimes known as disturbances. Another duty of the LFC is to control the tie-line power exchange error [1] that might exist in the MAPS. For simplicity, multi-area generating units are connected through tie-line communication link in other to ease the LFC. ...
... The value τ represents the small time delay which might varies from 0.1 to 1 s with consideration of two control areas for , = 1,2( ≠ ). Therefore, the modeled system with the information of the communication time delays is as (1). ...
p>In this paper, load frequency regulator based on linear quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, the controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.</p
... This problem brings out an aspiration to develop a precise and effective control mechanism in power system modeling known as load frequency control (LFC) [3], [4]. The main function of LFC in multi-area interconnected power systems is to maintain system performance measurements, such as area frequency and interchange tieline power, at their designated values [5]. Therefore, it is necessary to implement a control strategy that not only achieves frequency stabilization and maintains the output power but also obtains zero steady-state error and prevents unintended scheduled power exchange. ...
... The fluctuation in generating units is launched by the boiler dynamic control action and turbine control valves. A boiler dynamic can be defined as an instrument responsible for generating steam under pressure, and the structure of boiler 5 This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication. ...
This paper proposes an optimal α-variable model-free adaptive barrier-function fractional-order nonlinear sliding mode control (α(t)-MF-ABFFONSMC) for the load frequency control (LFC) problem of a four-area interconnected hybrid power system with boiler dynamics and physical constraints. The proposed α(t)-MF-ABFFONSMC is comprised of the ultra-local model (ULM)-based sliding mode disturbance observer (SMDO), proportional-differential (PD) controller, and adaptive barrier-function fractional-order nonlinear sliding mode control (ABFFONSMC). The ULM mechanism is utilized to re-formulate the complex four-area interconnected hybrid power system so as to reduce the controller’s design complexity, wherein SMDO is utilized to observe and eliminate the uncertain dynamics or lumped disturbance. Then, the SMDO based-iPD controller is designed. However, there always exists non-null estimation error under the SMDO method and the control performance cannot be ensured. Therefore, the ABFFONSMC is proposed and inserted into the SMDO-iPD controller to avoid the impact of estimation error and improve the control performance. In addition, an adaptive gain based on barrier function is formulated to approximate the upper bound of SMDO’s estimation error and thus decrease the undesired chattering on the sliding surface. Correspondingly, the α(t)-MF-ABFFONSMC is established. Moreover, the parameter optimizer based on the Marine Predator Algorithm (MPA) is proposed to tune the parameters of the proposed α(t)-MF-ABFFONSMC controller. Furthermore, using the Lyapunov theorem, the stability of α(t)-MF-ABFFONSMC via a closed-loop system is verified. To validate the performance of the proposed controller, the numerical simulation on a four-area interconnected hybrid power system is carried out in a Matlab/Simulink environment. The corresponding simulation results are presented to show the superiority and effectiveness of the proposed technique.
... The classic proportional integral derivative (PID) controller is still one of the most popular and widely used controllers in the power industry, which is widely used in power system LFC due to its simplicity, ease of use, fast performance, and stability [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In LFC systems related to power systems, a number of PID controllers are used in order to improve frequency stability; among these PID controllers [11,12] are: PID controllers whose parameters are optimized using the ICA [13] (see list of abbreviations at the end of the article), PID controllers whose parameters are optimized using the PSO [14], PID controllers whose parameters are optimized using the EHO [15], PID controllers whose parameters are optimized using the ACO [16], fuzzy PID controllers [17], fuzzy PID controllers whose parameters are optimized using the optimization algorithm based on novel HLUS-TLBO [18], fuzzy PID controllers whose parameters are optimized using the DE algorithm [19], fuzzy PID controllers whose parameters are optimized using the HDE-PS algorithm [20], fuzzy PID controllers whose parameters are optimized using the PSO [21], fuzzy PID controllers whose parameters are optimized using ACO [22], fuzzy PID controllers whose parameters are optimized using the HFA-PS algorithm [23], control fuzzy PID controllers whose parameters are optimized using MBA [24], and fuzzy PID controllers whose parameters are optimized using FA [25]. ...
... The classic proportional integral derivative (PID) controller is still one of the most popular and widely used controllers in the power industry, which is widely used in power system LFC due to its simplicity, ease of use, fast performance, and stability [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. In LFC systems related to power systems, a number of PID controllers are used in order to improve frequency stability; among these PID controllers [11,12] are: PID controllers whose parameters are optimized using the ICA [13] (see list of abbreviations at the end of the article), PID controllers whose parameters are optimized using the PSO [14], PID controllers whose parameters are optimized using the EHO [15], PID controllers whose parameters are optimized using the ACO [16], fuzzy PID controllers [17], fuzzy PID controllers whose parameters are optimized using the optimization algorithm based on novel HLUS-TLBO [18], fuzzy PID controllers whose parameters are optimized using the DE algorithm [19], fuzzy PID controllers whose parameters are optimized using the HDE-PS algorithm [20], fuzzy PID controllers whose parameters are optimized using the PSO [21], fuzzy PID controllers whose parameters are optimized using ACO [22], fuzzy PID controllers whose parameters are optimized using the HFA-PS algorithm [23], control fuzzy PID controllers whose parameters are optimized using MBA [24], and fuzzy PID controllers whose parameters are optimized using FA [25]. In [26], the fuzzy PID controller whose coefficients are optimized using GA is used to improve the frequency of the power system. ...
The penetration of intermittent wind turbines in power systems imposes challenges to frequency stability. In this light, a new control method is presented in this paper by proposing a modified fractional order proportional integral derivative (FOPID) controller. This method focuses on the coordinated control of the load-frequency control (LFC) and superconducting magnetic energy storage (SMES) using a cascaded FOPD–FOPID controller. To improve the performance of the FOPD–FOPID controller, the developed owl search algorithm (DOSA) is used to optimize its parameters. The proposed control method is compared with several other methods, including LFC and SMES based on the robust controller, LFC and SMES based on the Moth swarm algorithm (MSA)–PID controller, LFC based on the MSA–PID controller with SMES, and LFC based on the MSA–PID controller without SMES in four scenarios. The results demonstrate the superior performance of the proposed method compared to the other mentioned methods. The proposed method is robust against load disturbances, disturbances caused by wind turbines, and system parameter uncertainties. The method suggested is characterized by its resilience in addressing the challenges posed by load disturbances, disruptions arising from wind turbines, and uncertainties surrounding system parameters.
... Initially, the LFC investigations were centered on a single-area system [3], later extended to two area and multi-area systems [4]. The main objective of control schemes in LFC is to improve transient along with steady-state performances [5][6][7][8]. Among the available control schemes, the most preferred due to its low-order fixed structure, robustness, reliability, and easy implementation is the proportional integral derivative (PID) B Pooja Sharma 2017ree9082@mnit.ac.in ...
... 3, and design of PID-based LFC systems considering the CTD with simulation results are demonstrated in Sect. 4. The conclusions of the proposed approach are then discussed in Sect. 5. Notations || f || represents the absolute value of | f |, ||| f ||| denotes the norm value of | f |. | f | is used to represent the value of the angle of | f |. k p , k i , and k d are the PID controller parameters for LFC system of a single area. ...
The open communication network in the secondary loop of the load frequency control (LFC) scheme introduces the time delay while transmitting the control signals. The communication time delays (CTDs) affect the load frequency control (LFC) system's performance, leading to system instability. This paper proposes a robust method for analysis using a graphical technique for proportional integral derivative (PID)-based LFC systems considering CTD for single-area and multi-area. Moreover, the LFC system with CTD is expressed by its transfer function. Then, stability regions are plotted using the stability boundary locus method to compute the set of stabilized PID controller parameters for the LFC systems having delays. The notable features of the proposed approach are determined using frequency response, maximum sensitivity, and H ∞ criteria in the presence of CTD. The communication time delay is assumed to be additive uncertainty weight. Finally, stability regions are depicted in the controller parameter spaces. Finally, case studies are carried out for LFC systems considering CTD of a single-area and three-area isolated power systems. Simulation results as stability regions have demonstrated as (k p , k i), (k p , k d), and (k d , k i) planes for LFC system with the delay of a single-area and three-area isolated power system. These results illustrate the effectiveness of the proposed method for LFC system with CTD and provide robust performance.
... Initially, the LFC investigations were centered on a single-area system [3], later extended to two area and multi-area systems [4]. The main objective of control schemes in LFC is to improve transient along with steady-state performances [5][6][7][8]. Among the available control schemes, the most preferred due to its low-order fixed structure, robustness, reliability, and easy implementation is the proportional integral derivative (PID) B Pooja Sharma 2017ree9082@mnit.ac.in ...
... 3, and design of PID-based LFC systems considering the CTD with simulation results are demonstrated in Sect. 4. The conclusions of the proposed approach are then discussed in Sect. 5. Notations || f || represents the absolute value of | f |, ||| f ||| denotes the norm value of | f |. | f | is used to represent the value of the angle of | f |. k p , k i , and k d are the PID controller parameters for LFC system of a single area. ...
The open communication network in the secondary loop of the load frequency control (LFC) scheme introduces the time delay while transmitting the control signals. The communication time delays (CTDs) affect the load frequency control (LFC) system’s performance, leading to system instability. This paper proposes a robust method for analysis using a graphical technique for proportional integral derivative (PID)-based LFC systems considering CTD for single-area and multi-area. Moreover, the LFC system with CTD is expressed by its transfer function. Then, stability regions are plotted using the stability boundary locus method to compute the set of stabilized PID controller parameters for the LFC systems having delays. The notable features of the proposed approach are determined using frequency response, maximum sensitivity, and H∞ criteria in the presence of CTD. The communication time delay is assumed to be additive uncertainty weight. Finally, stability regions are depicted in the controller parameter spaces. Finally, case studies are carried out for LFC systems considering CTD of a single-area and three-area isolated power systems. Simulation results as stability regions have demonstrated as (kp,ki),(kp,kd),and(kd,ki) planes for LFC system with the delay of a single-area and three-area isolated power system. These results illustrate the effectiveness of the proposed method for LFC system with CTD and provide robust performance.
... been solved using the concept of FLC with existing controllers, and the comparative dynamics response of the system shows better dynamics with FLC over the existing controller. Author in [154][155][156][157] have presented a novel concept of fuzzy controller to improve the dynamics of short-term PS. Further, the type-2 fuzzy logic concept has been introduced by the author in [158,159] for AGC studies. ...
... In AGC study, there are various conventional performance index criteria (PICs) such as ISE, ITSE, IAE, and ITAE have been utilized in order to achieve optimal performance of controller parameters using various optimization strategies. The mathematical expression of various conventional PICs in AGC is given by Eq. (2-5) [86,167,170,171] Differential evolution I, PI/PID/PIDF, PIDF [173][174][175][176][177] Particle swarm optimization I, PI, PIDN, PI-PD [178][179][180] Firefly algorithm I, PI, PID, PID, FUZZY-PID [181][182][183][184] Bacterial Foraging Optimization PI, PID, PIDN, FO-PID [184][185][186][187][188] Artificial Bee Colony Optimization PID, FOPI-FOPD, PID µ [110,[190][191][192][193][194] Sine-cosine Algorithm I, PI, PID, FOPI-FOPD, FOPI-FOPID, Fuzzy-PI, PID [194][195][196][197] Grey wolf optimization PID, FUZZY-PID, PIDF [198][199][200][201][202][203][204] Grasshopper algorithm PDF + (1 + PI), PI, PIDN, PID, I-TD, TID [128,142,[205][206][207] Cuckoo search 2DOF-PI,2DOF-PID,2DOF-IDD,2DOF-PIDN, PIDN, PIDN-FOI, 2DOF-PIDN)-FOI [208][209][210][211] Crow search algorithm PI, PID, FO-PI, FO-PID, FO-PDN, FOI-PDN, TIDN [212,213] Bird swarm algorithm PI, PID, 2DOF-PI, 2DOF-PID, TID, I-TD, 2DOF-TID,3DOF-TID,3DPF-TIDN Hybrid-GS-PSA Fuzzy-PID [118] HALO-PS F-PID [126] Hybrid Dragonfly-PSA FPID [132] HSS-DE PI, PID [150,157] DE-PSO PID, Fuzzy PI/PID [176] hybrid PSO-PS Fuzzy PI [209] HCS-PSO FO-PDN, where, ΔF i , is change in frequency in ith areas, ΔP tie j-k is change in power among control area-j and area-j and time (t). ...
This article aims to provide an in-depth analysis of the recent development of various control strategies and their implementation concerning frequency and power control in automatic generation control (AGC) systems. The design of a secondary controller is an important aspect of AGC for effective automatic generation control system operation. Various secondary controllers like integer order, cascade, fractional order, degree of freedom, modern/intelligence controller, and some recently developed controllers used in AGC are explained briefly with their control structure. Various optimal control theories, such as conventional optimization techniques, heuristic/metaheuristic optimization techniques, and their utilization in AGC to get the optimal performance of secondary controllers, are described in detail. Moreover, various performance index criteria(PICs), such as conventional and modified PICs, are also briefly explained. Finally, this paper concludes with an emphasis on the controller design, application of various optimal control theories, and various PICs used in the field of AGC. This article helps the researcher to bridge the gap between current developments, implementation, technical challenges, and anticipated trends in AGC studies.
... Sondhi and Hote [9] recommended the use of a fractional order PID controller for LFC in single and multi-area power systems with non-reheated, reheated, and hydro turbine models. New hybrid differential evolution and pattern search (HDEPS) was employed by Sahu et al. [10] to obtain gains of a fuzzy PI/PID controller for LFC of a twoarea power system with non-reheated and reheated turbine models. The proposed control method was compared with the modern heuristic optimization techniques given in the literature and usually better performances were achieved. ...
... Yet, this does not affect the calculations of the stabilizing PD controller gains [38]. Therefore, ω is varied in the range [ε, ∞] for obtaining the SBL where ε is a very small number chosen by the designer for avoiding discontinuity in Eq. (10). The line K f = K f (0), which is obtained by writing ω = 0 in (9), limits the SBL [35]. ...
Load frequency control (LFC) is an important control problem as it determines the quality of power generation by controlling the system frequency and inter-area tie-line power. To maintain a good quality power supply, LFC must be robust against unknown external disturbances and parameter variations of the power system. Therefore, this paper presents the design of PI–PD controllers, which are robust against parameter changes and have good disturbance suppression capability, for load frequency control of a single-area single- or multi-source power system. PI–PD controller parameters were obtained by applying the weighted geometric center method to the stability boundary locus of the closed-loop control system. The approach was applied to both the inner and outer loops of the PI–PD control system structure, sequentially. Performance and robustness of the proposed PI–PD control system are evaluated using some well-known integral error criteria values, settling time, and peak value (overshoot) in the analysis of the power system with both nominal values and ± 50% changes in the system parameters. The simulation results show that the designed PI–PD controller effectively limits the effect of load disturbance and variations in system parameters.
... In their efforts to address this problem, researchers in the field have developed various algorithms [7], which can be classified into two broad groups. One consists of control algorithms for the controller, including PID [8,9], sliding mode control (SMC) [10], active disturbance rejection control (ADRC) [11], fractional order PID (FOPID) This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 The Authors. ...
... Moreover, PSO [20], GA [27], and PROP [1] are introduced as the optimization algorithms for power distributor in this paper. In addition, PSO optimized fuzzy FOPI controller (PSO-Fuzzy-FOPI) [12], fuzzy FOPI controller (GA-Fuzzy-PI) [13], Takagi Sugeno fuzzy PI controller (TS-fuzzy-PI) [14], and PI controller optimized by PSO (PSO-PI) [8], PI optimized by GA (GA-PI) [9], Fuzzy-FOPI [14], FOPID algorithm [12], PI algorithm are used as control algorithms for the controller. The combination of the controller and power distributor with above algorithms (combined algorithm) is used as a comparison Sizes of pools 1 and 2 1000000 ...
Abstract To reduce the total power generation cost and improve the frequency stability of an island microgrid integrating renewable energy generation sources, a data‐driven cooperative load frequency control (DC‐LFC) method is proposed for solving the coordination control problem occurring between the controller and power distributor of the system. A novel algorithm, termed the effective exploration‐distributed multiagent twin‐delayed deep deterministic policy gradient (EED‐MATD3) algorithm, is further proposed, the design of which is structured based on the concepts of imitation learning, ensemble learning, and curriculum learning. The EED‐MATD3 method employs various exploration strategies, and the controller and power distributor are treated as two agents. Through centralized training and decentralized execution, a robust cooperative control strategy is realized. The performance of the proposed algorithm is verified in an LFC model of Zhuhai Tandang Island, an island microgrid in the China Southern Power Grid.
... The job of frequency regulator otherwise known as the LFC is to retain the regulation frequency over randomly active power load changes sometimes known as disturbances. Another duty of the LFC is to control the tie-line power exchange error [1] that might exist in the MAPS. For simplicity, multi-area generating units are connected through tie-line communication link in other to ease the LFC. ...
... In this section, the MAPS with time delay are recommended to test the feasibility of the proposed LQG controller. The delays in the area control error signals of1 ...
In this paper, load frequency regulator based on Linear Quadratic Gaussian (LQG) is designed for the MAPS with communication delays. The communication delay is considered to denote the small time delay in a local control area of a wide-area power system. The system is modeled in the state space with inclusion of the delay state matrix parameters. The Kalman filter in combination with Linear quadratic regulator is used for the proposed LQG based load frequency control (LFC). The state space model is used to derive the optimal feedback gain for the LQG. Since some state variables are difficult to measure in a real modern multi-area power system, Kalman filter is used to estimate the unmeasured variables. In addition, both estimates with optimal feedback for LQG in one hand and time-delay in the other hand are used as input signal of the controller. The controller with the optimal feedback gain reduces the frequency spikes to zero and keeps the system stable. Lyapunov function based on the LMI technique is used to re-assure the asymptotically stability and the convergence of the estimatimator error. The designed LQG is simulated in a two area connected power network with considerable time delay. The result from the simulations indicates that the controller performed with expectation in terms of damping the frequency fluctuations and area control errors. It also solved the limitation of other controllers which need to measure all the system state variables.
... Electric power systems face increasing challenges owing to renewable integration, interconnection expansion, and deregulation (Haroun and Li, 2019), making frequency stability and power transmission reliability during disturbances more difficult. Load Frequency Control (LFC) is crucial for regulating frequency deviations and tie-line power flows across interconnected control areas (Sahu et al., 2014;Panda et al., 2009), but traditional strategies have limitations in responding to significant transients (Haroun and Li, 2017). ...
This work focuses on load frequency control in interconnected power systems, a critical aspect of modern power grid operations. However, sudden load disturbances and generator outages can lead to transient oscillations between control areas, posing challenges to frequency control. The aim of the work was to investigate and enhance load frequency control behaviour, considering dynamic load changes and uncertainties. Fuzzy Logic Controllers optimized with Particle Swarm Optimization were applied to improve control robustness. The Particle Swarm Optimisation algorithm was used to tune the scaling factors and parameters of the fuzzy controllers to optimize their performance. The methods were tested on a standard four-area interconnected power system model equipped with load frequency control blocks, reheaters, governors, rate constraints, and thermal components. Different disturbance scenarios including parameter fluctuations and load changes were evaluated. The Fuzzy Logic Controllers demonstrate resilient response across scenarios without needing extensive tuning. Particle Swarm Optimization improves robustness through systematic exploration for constraint-based nonlinear optimization. Tuning fuzzy controllers with bio-inspired algorithms enhances efficiency in addressing complex grid conditions. The results provide insights into designing more secure and resilient grid controls, contributing to power system stability research. KEYWORDS: Load Frequency Control, Interconnected Power Systems, Fuzzy Logic Controllers, Particle Swarm Optimization, Robust Control.
... Different type of generating units and their effects have been studied e.g. thermal with reheat [4], generation rate constraint (GRC) [5], reheat and battery energy storage both the areas [6], hydro turbine and hydro-governor in both the areas [7], thermal with reheat turbine along with hydro and gas turbine plants in both the areas [8], etc. ...
Fractional order proportional-integral-derivative (FOPID) controllers are designed for load frequency control (LFC) of two interconnected power systems. Conflicting time domain design objectives are considered in a multi objective optimization (MOO) based design framework to design the gains and the fractional differ-integral orders of the FOPID controllers in the two areas. Here, we explore the effect of augmenting two different chaotic maps along with the uniform random number generator (RNG) in the popular MOO algorithm - the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Different measures of quality for MOO e.g. hypervolume indicator, moment of inertia based diversity metric, total Pareto spread, spacing metric are adopted to select the best set of controller parameters from multiple runs of all the NSGA-II variants (i.e. nominal and chaotic versions). The chaotic versions of the NSGA-II algorithm are compared with the standard NSGA-II in terms of solution quality and computational time. In addition, the Pareto optimal fronts showing the trade-off between the two conflicting time domain design objectives are compared to show the advantage of using the FOPID controller over that with simple PID controller. The nature of fast/slow and high/low noise amplification effects of the FOPID structure or the four quadrant operation in the two inter-connected areas of the power system is also explored. A fuzzy logic based method has been adopted next to select the best compromise solution from the best Pareto fronts corresponding to each MOO comparison criteria. The time domain system responses are shown for the fuzzy best compromise solutions under nominal operating conditions. Comparative analysis on the merits and de-merits of each controller structure is reported then. A robustness analysis is also done for the PID and the FOPID controllers.
... The output of the fuzzy system is a fuzzy value and, therefore, must be converted to a real value using a suitable Defuzzification technique. The most effective ''center of gravity (COG)'' method of defuzzification is used to convert the fuzzy value to real value (Sahu et al., 2014). In Senapati et al. (2019b), a Takagi-Sugeno-FLC was used to enhance the transient response of the voltage of a standalone DC-MG. ...
The current widespread support of decarbonization and green energy has led to a notable increase in the incorporation of clean energy sources (CESs) in microgrids (MGs). CESs are intermittent, and if they become more widely used in MG, managing uncertainty will become more difficult. This is true even with the environmental and financial advantages of CESs. In this paper, the operation of a DC/AC MG, which integrates solar photovoltaics (PVs), wind farms, fuel cells (FCs), and battery chargers (BCs), is investigated and analyzed under uncertain conditions. The MG's main energy source is thought to be the PV, while the FC and BC assist in maintaining the MG's stability. A variable AC load and an electric vehicle charging system are fed by the MG. Two control system approaches have been designed and evaluated. The first is a new design of fuzzy logic controller (FLC), which is provided and applied to provide an adequate energy management system (EMS) for the investigated MG considering uncertainties of CESs. Moreover, JAYA-based optimal control has been developed. The proposed EMS is utilized to adapt the fuel consumption for the FC and the charging concept of Li-ions and to provide a constant load bus voltage. In order to demonstrate the effectiveness of the suggested technique, the proposed novel design of FLC and JAYA-based controllers' performance is tested under partial shadowing of the PV with abrupt load fluctuations of 25% and contrasted with the PI controller methodology, where it is designed using the Ziglar Nicolas technique. The obtained findings show how the suggested control technique improves the system and the MG's dynamic performance. A MATLAB\Simulink simulation is carried out, and the outcomes demonstrate the effectiveness and superiority of the suggested strategy in managing uncertainty.
... The output of the fuzzy system is a fuzzy value and, therefore, must be converted to a real value using a suitable Defuzzification technique. The most effective ''center of gravity (COG)'' method of defuzzification is used to convert the fuzzy value to real value (Sahu et al., 2014). In Senapati et al. (2019b), a Takagi-Sugeno-FLC was used to enhance the transient response of the voltage of a standalone DC-MG. ...
The current widespread support of decarbonization and green energy has led to a notable increase in the incorporation of clean energy sources (CESs) in microgrids (MGs). CESs are intermittent, and if they become more widely used in MG, managing uncertainty will become more difficult. This is true even with the environmental and financial advantages of CESs. In this paper, the operation of a DC/AC MG, which integrates solar photovoltaics (PVs), wind farms, fuel cells (FCs), and battery chargers (BCs), is investigated and analyzed under uncertain conditions. The MG's main energy source is thought to be the PV, while the FC and BC assist in maintaining the MG's stability. A variable AC load and an electric vehicle charging system are fed by the MG. Two control system approaches have been designed and evaluated. The first is a new design of fuzzy logic controller (FLC), which is provided and applied to provide an adequate energy management system (EMS) for the investigated MG considering uncertainties of CESs. Moreover, JAYA-based optimal control has been developed. The proposed EMS is utilized to adapt the fuel consumption for the FC and the charging concept of Li-ions and to provide a constant load bus voltage. In order to demonstrate the effectiveness of the suggested technique, the proposed novel design of FLC and JAYA-based controllers' performance is tested under partial shadowing of the PV with abrupt load fluctuations of 25% and contrasted with the PI controller methodology, where it is designed using the Ziglar Nicolas technique. The obtained findings show how the suggested control technique improves the system and the MG's dynamic performance. A MATLAB\Simulink simulation is carried out, and the outcomes demonstrate the effectiveness and superiority of the suggested strategy in managing uncertainty.
... In actual production, the tea water-removing temperature control is a complex nonlinear problem that is difficult to be solved by traditional PID. Nevertheless, fuzzy control can solve complex control problems under nonlinear and real-time changes very well, owing to it does not need to build an exact mathematical model (Hamza et al. 2017;Sahu et al. 2014). In view of this, we design an adaptive fuzzy PID controller for tea water-removing temperature control by combining fuzzy logic theory and PID controller technology. ...
Accurate temperature control in tea water-removing is the key to determine the quality of tea. The PID controller is universally used in this field. However, the choice of controller parameters has a direct effect on performance. Traditional method based on manual experience normally leads to subjectivity, long cycle, and large fluctuations. Therefore, this paper proposes an improved sparrow search algorithm to tune the quantization and scale factor of the fuzzy PID controller. Firstly, the adaptive fuzzy controller for the tea cylinder water-removing machine is established. Secondly, in addition to the levy flight strategy, random mutation and perturbation are introduced into the sparrows’ behaviour to enrich their diversity. Moreover, dynamic staged and real-time feedback adjustment strategies are also proposed to increase the diversity of the population size. Finally, traditional methods and four prior arts are compared with the proposed method to verify the superiority. The simulation results show that the fuzzy PID controller optimized by CSDFSA has excellent performance in terms of stability and dynamic response. Additionally, this controller also exhibits significant robustness under external signal interference, which can improve the performance and intelligence level of the tea water-removing temperature control system. In conclusion, this manuscript proposes an intelligent fuzzy control system through the intersection of computer science and agricultural engineering and applies it to the processing of tea. With the help of computer technology to promote the development of the famous tea industry.
... The present work is an attempt to make use of Taguchi optimisation technique to optimise cutting parameters during high speed turning of Inconel 718 using cermet tool. The performance of the cermet tool is described using response surface methodology (RSM (Sahu et al., 2014)a novel hybrid Differential Evolution (DE. ...
Most of the time, thin walls may be developed during milling operations to manufacturecomplicated dies and moulds. In current high-speed machining, bending these thin wallsis expected owing to thermal expansion, making it a common source of dimensional error.Workpiece temperature (WT) with a response Machine time has been considered outputresponses. By adopting a unique technique, the response surface methodology during 2.5 Dmilling of Inconel 718, an anti-corrosive material, we could reach the best process parameterscombination for minimizing workpiece temperature. The workpiece’s temperature wasdetermined by three sets of combinations of k-type thermocouples while the surface testermeasured surface quality. Box–Behnken design has been used to conduct 26 trials to determinethe best combinations of parameters. Several process variables were examined, includingcutting speed, feed per tooth, depth of cut, and tool nose radius, and ANOVA has been usedto quantify the impact of these variables. A multi-objective genetic algorithm based on theregression model has been applied to optimize the parameters. Five structural experiments werealso conducted to verify this optimized process parameters combination, which was provenmore successful.
... Therefore, the observer bandwidth ω o should be larger than the controller bandwidth ω c , but the excessive observer bandwidth is difficult to be realized in practical engineering and wastes the control output. In addition, for the controller bandwidth ω c , the excessive bandwidth will also waste the control energy, and it is difficult to suppress the road noise signal of too small a bandwidth (Mosquera-Sánchez et al., 2017;Sahu et al., 2014). To sum up, the objective function in Eq. (43) is established to optimize the extended-state observer bandwidth ω o . ...
Due to the inherent dynamic coupling between mechanical components such as the steering system and suspension system, the vertical external input will affect the lateral movement of the chassis, which makes it difficult to track the ideal trajectory when complex excitation conditions exist. To solve the abovementioned problems, the X-by-wire chassis is taken as the research object in this work, and the coupling dynamic model is established. Then, based on proving the reversibility of the coupling dynamic model, a pseudo-linear composite system is proposed to decouple the lateral and vertical signals of the chassis system. Next, the decoupling active disturbance rejection (DADR) trajectory-tracking control strategy is proposed. And a multi-objective optimization method of the bandwidth parameters of the DADR trajectory-tracking controller is proposed according to its convergence conditions. Experiments show that the proposed control strategy can effectively suppress the vehicle roll and yaw motion caused by the lateral–vertical dynamic coupling in the process of trajectory tracking to realize the accurate tracking of the ideal trajectory.
... In PID design, the most taxing exercise is finding the optimal gains suitable for the desired outcome, i.e., minimization of frequency deviation in the LFC scenario [15]. Multiple versions of problem-specific PID design techniques and optimization algorithms are available in the literature, such as differential evolution (DE) hybridized with pattern search (PS) [16]optimization using integral error criterion [17]. Tuning the PID parameters using conventional methods like the Ziegler-Nicholas method or Cohen-coon can result in poor damping; the dominant pole method can be tedious as it is sometimes impossible to define the dominant poles [18]. ...
The imbalances created by generation and load can indeed result in frequency variations demanding a load frequency control (LFC). The LFC becomes even more complicated with the multi-source distributed hybrid power system (HPS), where designing optimal controller parameters by identifying the region of convergence (RoC) is critical as it guarantees the HPS's stability. Hence, this paper aims to address the frequency regulation (FR) issue in a state-of-the-art modeled multi-source distributed HPS with a coordinated control approach. The modeled HPS includes a reheat thermal power system (RTPS), wind turbine generator (WTG), fuel cell (FC) stack, battery energy storage system (BESS), diesel engine generator (DEG), and various controllers. A novel method called parameter sensitivity algorithm (PSA) is proposed to obtain the RoC of the controller gains and is further optimized using a constrained genetic algorithm (GA). To demonstrate coordinated control effectiveness, a hybrid objective function for optimization is formulated as constrained GA's fitness function using the integral time absolute error (ITAE) and integral absolute error (IAE). Furthermore, a comparative assessment for PI, PID with filter (PIDN), cascaded PI-PD with filter (PIPDN) controller topologies is carried out using error index-based performance metrics to ascertain the preeminence of the hybrid objective function over existing objective functions. The investigation results validate that the suggested optimized controllers provide enhanced FR and are robust to uncertainties. The practical feasibility of the proposed methodology is reinforced using dSPACE hardware in loop testing.
... In this section, IT2FS was used to control a 3-PRS parallel robot model using the novel membership function. [30]. The simulation was run for 120 seconds with a unit phase input as a reference signal. ...
The general effectiveness of fuzzy representation depends heavily on the membership functions. The best footprint of uncertainty for a specific fuzzy system is highly dependent on the nature of the problem that the fuzzy system is supposed to solve. As a result, a proper footprint of uncertainty size selection is needed to improve the fuzzy system's efficiency. This research intends to investigate the impact of a unique linear parallelogram membership function's footprint of uncertainty on nonlinear system modeling and control. The proposed type-2 membership function has a crisp membership degree at the end points of the footprint of uncertainty and uncertain values in-between. When dealing with data whose membership degree is certain at the boundary but uncertain in-between, the proposed membership function having its highest uncertainty at the midpoint of the membership function width is advantageous. Tuning the parameters of the proposed MF will provide a variety of triangular and quadrilateral footprints of uncertainty shapes that will better capture the training data's uncertainties. The gradient descent learning algorithm was used to tune the consequent parameters of the evaluated interval type-2 fuzzy system. The performance results demonstrated the effect of the footprint of uncertainty on linear parallelogram membership function-based fuzzy system's capability in prediction, identification, and control tasks.
... (iv) The fuzzy inference system's output (which is the fuzzy value that is transformed into real value by the defuzzification method) [16]. However, there are many MFs in the FLC; the triangular MFs are commonly accepted in FLC strategies because of their realtime applications, economical nature, and improved performance [39]. The fuzzy controller with scaling factors, where five linguistic variables of MFs for inputs and output of fuzzy are used, was planned in [40] and was used effectively used many times in the literature [2,15,16]. ...
This paper suggests a fuzzy logic controller (FLC) structure from seven membership functions (MFs) and its input–output relationship rules to design a secondary controller to reduce load frequency control (LFC) issues. The FLC is coupled to a proportional–integral–derivative (PID) controller as the proposed FPID controller, which is tuned by an optimized water cycle algorithm (WCA). The proposed WCA: FPID scheme was implemented with two models from the literature under the integral time absolute error cost function. Initially, a two-area non-reheat unit was implemented, and the gains of PID and FPID controllers were adjusted to verify the suitability of WCA in solving LFC issues. Then, in order to identify the robustness of the closed-loop system, sensitivity analysis is carried out. Additionally, a two-area non-reheat unit was tested under the governor dead band nonlinearity. To guarantee the suitability of the proposed FPID controller, a model with a mixture of power plants, such as reheat, hydro, and gas unit in each area was carried out with and without the HVDC link, which can increase practical issues with LFC. The proposed controller's robustness was studied for all models under numerous scenarios, step load perturbations (SLP), and different objective functions. Simulation results proved that the proposed FPID controller provided superior performance compared to recently reported techniques in terms of peaks and settling time.
... This proves that the DE technique gives better performance than the FA technique. It can be seen from Sahu, and Panda 2014). The dynamic response revealed in Figure 7(a-f) represents that responses with the proposed DE-based hPIDN-FOPD controller are superior to published FAbased PID (Padhan, Sahu, and Panda 2014).To prove the usefulness of the hPIDN-FOPD structure, a random disturbance (Debbarma, Saikia, and Sinha 2014), as revealed in Figure 8, is assumed in area-1, and the response is shown in Figure 9. From Figure 9, it can be concluded that the projected method demonstrates an improved response to the published FA-based PID (Padhan, Sahu, and Panda 2014). ...
In this work, various controllers, such as hybrid PID with Filter and Fractional-Order PD (hPIDN-FOPD), hPIDN-PD and PID, are recommended for frequency control problems. The analysis is prepared for two electrical power system (PS) models, i.e. two-area and three-area thermal systems with physical restraints. The benefit of the suggested Differential Evolution (DE)-based hPIDN-FOPD controller has been shown by relating its performance with some newly published approaches, such as Bacteria Foraging Optimisation Algorithm (BFOA), Genetic Algorithm (GA), Firefly Algorithm (FA) and hybrid BFOA and Particle Swarm Optimisation (hBFOA-PSO) techniques. The recommended approach is supplementarily investigated to a three-area thermal PS with non-linearity. The analysis reveals on the comparison that DE-based hPIDN-FOPD offers improved results related to PID, hPIDN-PD and the newly available FA-tuned PID controller. The experimental authentication with the Hardware-in-the-Loop (HIL) real-time (RT) simulation was carried out on the OPAL-RT-OP5700 simulator.
... Hence, fuzzy-aided classical controllers are gaining momentum, especially in the power system optimization domain, compared to classical controllers alone. Different fuzzy (F)-aided classical controllers that have been reported in the recent literature, such as bacteria foraging optimization (BFO) [22]/ICA [23]/tuned FPI, DEPS-tuned [24] FPI/FPID, Type-II FPID [25] based on the GWO sine cosine approach, modified DE-approach-based FPID [26], FA-tuned FPID [27], symbiotic organism search (SOS)-based FPID [28], SOA-optimized FOFPID [29], etc., are available. ...
In this paper, a fuzzy (F) proportional (P)-integral (I)-derivative (D) (PID) (FPID) controller optimized with a water cycle algorithm is proposed for load frequency control of a mul-ti-area multi-fuel (MAMF) power system. The MAMF system has the realistic feature of communication time delays (CTDs), in order to conduct an analysis nearer to realistic practice. Initially, the MAMF system is analyzed when subjected to a step load disturbance (SLD) of 10% on area 1. The superiority of the fuzzy PID controller is revealed upon comparing it with PID plus double derivative (DD) (PIDD) and PID controllers. The MAMF system is investigated with and without CTDs, to demonstrate their impact on system performance. Later, an additional HVDC line is incorporated in parallel with the existing AC line for further enhancement of the system performance. Finally, the MAMF system is targeted with random loading to validate the robustness of the presented control scheme.
... Fuzzy PID tuned by Teaching Learning Based Optimization (TLBO) for dual area power system is studied in [14]. A novel hybrid DE and Pattern Search (PS) has been used to tune the scaling factor gains of fuzzy PI/PID controllers employed for LFC in a two-area power system [15]. The most recent controllers based on different strategies employed for LFC in power systems is concluded in [16]. ...
... LFC in power systems is the most significant in order to provide or supply reliable active power with a good quality [5]. Consequently, the tasks of LFC in interconnected multi-areas are to provide [11] IJESRT is licensed under a Creative Commons Attribution 4.0 International License. the desired real power output from the generator to meet the change in load, maintain the frequency of the power system close to the nominal value and maintain the scheduled interchange of power between control areas through tie-lines [6,7]. Thus, the control scheme is required, that not only preserves constancy of frequency and desired output power but also attain zero steady-state error and inadvertently scheduled interchange. ...
This article addresses the application of active disturbance rejection control (ADRC) and classical PID controller to distributed load frequency control (LFC) for an interconnected multi-area power system. The proposed approach has been designed to improve the power system performances as well as to reduce the damping oscillations of the uncertainties due to variation in system parameters and load perturbations. Dynamic performances of an interconnected multi-areas power system are presented, and the effect of the generation rate constraint (GRC) is considered. The model was employed in the ADRC+PID architectures. Digital simulations for a 4-area power system are presented to validate the effectiveness of the proposed approach. The supremacy of the proposed controller is proved by comparing the results with that of the recently published paper based on the linear quadratic regulator (LQR) for the same system. The simulation results are compared among the proposed controller, ADRC technique alone, and conventional PID controller. Simulation results asserted that the proposed technique outperforms the other controllers in terms of fast settling time, and less in peak overshoot. The proposed ADRC+PID approach satisfies the LFC requirements with a reasonable dynamic response.
The exceptional growth in the penetration of renewable sources as well as complex and variable operating conditions of load demand in power system may jeopardize its operation without an appropriate automatic generation control (AGC) methodology. Hence, an intelligent resilient fractional order fuzzy PID (FOFPID)
controlled AGC system is presented in this study. The parameters of controller are tuned utilizing a modified moth swarm algorithm (mMSA) inspired by the movement of moth towards moon light. At first, the effectiveness of the controller is verified on a nonlinear 5-area thermal power system. The simulation outcomes bring out that the suggested controller provides the best performance over the lately published strategies. In the subsequent step, the methodology is extended to a 5-area system having 10-units of power generations, namely thermal, hydro, wind, diesel, gas turbine with 2-units in each area. It is observed that mMSA based FOFPID is more effective related to other approaches. In order to establish the robustness of the controller, a sensitivity examination
is executed. Then, experiments are conducted on OPAL-RT based real-time simulation to confirm the feasibility of the method. Finally, mMSA based FOFPID controller is observed superior than the recently published approaches for standard 2-area thermal and IEEE 10 generator 39 bus systems.
Frequency regulation in Multi-region Interconnected Power Systems (MIPS), incorporating wind turbine systems, energy storage units, and demand response, is a challenging control problem. The problem involves maintaining grid stability, integrating variable renewable energy sources, enhancing grid resilience, optimizing energy storage and demand response capabilities, and ensuring regulatory compliance. Effective frequency regulation is a key problem for reliable and sustainable power system operation. In this paper, complex and uncertain dynamics in various components of a MIPS are modeled using multiple first-order dynamic fractional-order Type-2 Fuzzy Logic Systems (T2-FLSs). The models are evaluated, and the best possible dynamic model is chosen. With the best possible model, an optimal controller is designed. The stability and optimality analysis are presented through a Linear Matrix Inequality (LMI) approach. The adaptive laws of T2-FLSs are derived such that some LMI-based conditions are satisfied. The designed controller does not depend on the dynamics of MIPS. The uncertainties of time-varying load, wind energy, and solar power are modeled using T2-FLSs. The suggested LMI technique presents adaptation laws for T2-FLSs to ensure stability in the presage of natural disturbances and errors. The designed scheme is validated by applying to a practical 39-bus IEEE test system that includes the wind farms, demand response, and energy storage systems. The simulation results under various conditions verify the usefulness of the suggested controller. The comparisons with conventional controllers demonstrate that the suggested approach is more effective under uncertainties and natural perturbations.
This paper proposes a data-driven Active Fault Tolerance Load Frequency Control (DDAFT-LFC) method for island microgrids. It is demonstrated that DDAFT-LFC can effectively prevent frequency control loss caused by sudden off-grid faults affecting regulating units and emergency faults in microgrids, as well as reduce the total generation cost and improve frequency stability of an island microgrid relying on a high renewable energy input. The method adopts an active fault tolerance strategy which can adapt to the complex stochastic environment, thus improving the robustness of the LFC strategy and achieving multi-objective optimization of dynamic performance and economic efficiency via regulation of the controller’s control strategy. In order to realize active fault-tolerance, this paper proposes a deep meta-deterministic policy gradient (DMDPG) algorithm, which employs a meta-reinforcement learning method to help the agent to perform multi-task collaborative learning in order to adapt to changes in microgrid environment parameters caused by changes in the unit combinations. The method is validated in an experiment of island microgrid of the China Southern Grid (CSG).
In load frequency control (LFC) of island microgrids, the objectives of the controller and power distributor are inconsistent, which increases the frequency deviation and total generation cost. To solve this problem, a data-driven multi-input multioutput LFC (MIMO-LFC) method is proposed. Furthermore, an innate-oriented large-scale multiagent double delayed deep deterministic policy gradient algorithm is proposed for this method. The design of the algorithm is based on a human cognitive mechanism, whereby
a priori
knowledge is delivered to the agent before prelearning to guide the learning of the agent more effectively, thus improving the robustness of MIMO-LFC. This method integrates the controller and distributor into one agent and solves the cooperative control problem arising between the controller and power distributor by outputting the command of each unit directly. An experiment on the Zhuzhou Island microgrid verifies the effectiveness of the proposed method.
Los servidores web constituyen una parte fundamental para el funcionamiento de las aplicaciones web y por estos fluye toda la información de
las entidades y personas. Estos son constantemente amenazados por ataques informáticos, los cuales aprovechan vulnerabilidades o problemas de seguridad
para lograr sus objetivos. Los administradores web y especialistas de seguridad informática, por medio de controles de seguridad tienen que garantizar la
preservación de la confidencialidad, integridad y disponibilidad de la información. En la presente investigación se desarrollan controles de seguridad
para los servidores web, que contribuyen a aumentar la protección de la información, disminuir las vulnerabilidades de software y por tanto disminuir las
posibilidades de éxito de un ataque informático. Los resultados obtenidos confirman su efectividad en la mitigación de vulnerabilidades y en el aumento de
la protección de la información.
Power system control and stability have been an area with different and continuous challenges in order to reach the desired operation that satisfies consumers and suppliers. To accomplish the purpose of stable operation in power systems, different loops have been equipped to control different parameters. For example, Load Frequency Control (LFC) is introduced to maintain the frequency at or near its nominal values, this loop is also responsible for maintaining the interchanged power between control areas interconnected via tie-lines at scheduled values. Other loops are also employed within power systems such as the Automatic Voltage Regulator (AVR). This thesis focuses on the problem of frequency deviation in power systems and proposes different solutions based on different theories. The proposed methods are implemented in two different power systems namely: unequal two-area interconnected thermal power system and the simplified Great Britain (GB) power system. Artificial intelligence-based controllers have recently dominated the field of control engineering as they are practicable with relatively low solution costs, this is in addition to providing a stable, reliable and robust dynamic performance of the controlled plant. They professionally can handle different technical issues resulting from nonlinearities and uncertainties. In order to achieve the best possible control and dynamic system behaviour, a soft computing technique based on the Bees Algorithm (BA) is suggested for tuning the parameters of the proposed controllers for LFC purposes. Fuzzy PID controller with filtered derivative action (Fuzzy PIDF) optimized by the BA is designed and implemented to improve the frequency performance in the two different systems under study during and after load disturbance. Further, three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA have also been implemented in the two-area interconnected power system. The robustness of these fuzzy configurations has been evidenced against parametric uncertainties of the controlled power systems Sliding Mode Control (SMC) design, modelling and implementation have also been conducted for LFC in the investigated systems where the parameters are tuned by the BA. The mathematical model design of the SMC is derived based on the parameters of the testbed systems. The robustness analysis of the proposed SMC against the controlled systems’ parametric uncertainties has been carried out considering different scenarios. Furthermore, to authenticate the excellence of the proposed controllers, a comparative study is carried out based on the obtained results and those from previously introduced works based on classical PID tuned by the Losi Map-Based Chaotic Optimization Algorithm (LCOA), Fuzzy PID Optimized by Teaching Learning-Based Optimization (TLBO)
This study presents an optimal online control that implements a biogeography-based optimization (BBO) algorithm on a battery energy system (BES) integrated into an energy-stored quasi-impedance source inverter (qZSI) that connects a photovoltaic (PV) power plant to the grid. The BBO algorithm was used to tune the PI regulator in the BES current control loop by minimizing the integral time absolute error (ITAE). Two different options for the BBO are compared in this application:1) a PI controller with online self-tuning based on BBO, and 2) a PI controller with offline tuning using BBO. Moreover, the BBO-based PI controllers were compared with a third controller tuned online using the particle swarm optimization (PSO) algorithm. To evaluate and compare the controllers, a PV power plant with a battery energy-stored qZSI was simulated under different operating conditions, such as step changes in the BES current reference, different sun irradiance, and a grid voltage sag. The results demonstrate better control of the BES current with the online tuning techniques (BBO and PSO) than with the offline tuning procedure, and similar results between the two online tuning algorithms. Nevertheless, throughout the simulation, the time of use of the BBO algorithm was almost 2.5 times smaller than the PSO algorithm. Therefore, the online BBO-based PI controller is considered the most suitable option.
Control systems are widely used in our lives, and good control can be achieved by obtaining the optimal tuning parameters of the control system. The number of parameters that need to be adjusted for different control systems varies. With an increase in tuning parameters, the difficulty of tuning grows. Therefore, this paper proposes an improved monkey multiagent DRL (IMM-MADRL) algorithm and selects 3 test functions to test the setting environment of 2–7 parameters. Thus, these parameters are adjusted. The IMM-MADRL algorithm is based on the modified monkey-multiagent DRL (MM-MADRL) algorithm, and its initialization method, position update method and somersault operation are further improved so that it can perform good parameter tuning for a control system with many parameters. The simulation part of this paper proves the advantage of the IMM-MADRL algorithm in a multiparameter control system.
This work focuses on investigating the adaptability of solid oxide fuel cell-gas turbine (SOFC-GT) hybrid system for ship application under uncertain factors. The effect of rapids, wind and waves on the performance of ship SOFC-GT is analyzed. In addition, a novel control system combining fuzzy logic theory, temperature feedforward and coordination factor on-line adjustment is proposed to address the problem of load disturbances caused by uncertain factors. The results show that the proposed operation strategy can shorten the thermal response time inside fuel cell stacks by almost 49.97%, meanwhile, reducing the maximum temperature changing rate at the electrochemically active tri-layer cell composed of anode, electrolyte, and cathode (PEN structure) by around 17.86%. Moreover, the reasonable matching between air flow and fuel flow is an essential prerequisite to ensure the safe and efficient operation of ship SOFC-GT. While the SOFC-GT is working at full load, the results indicate that the fuel to air ratio cannot exceed 2.56✕10⁻² g/g. Finally, an application scenario of the 5000-ton river-to-sea cargo ship sails from Nanjing Port to Yangshan Port (Eastern China) is conducted to analyze the operation characteristics of ship SOFC-GT under uncertain factors. Two set of 1000 kW SOFC-GT systems with the electrical efficiency of 64.66% is designed for the target ship, the results conclude that the operation strategy of each SOFC-GT system supports 50% load is beneficial in reducing the power tracking time and SOFC temperature overshoot. The average electrical efficiency of 61.45% and 61.04% are achieved in winter and summer typical days respectively in the whole voyage.
In order to achieve the fast load following and safe transient operation of Solid Oxide Fuel Cell-Gas Turbine hybrid system, a novel control approach by combining the multi control loops with the coordinated protection loops is proposed in this work. The coordinated protection loops can adjust the transient behavior of operation parameters on-line to avoid the undesired operation faults, such as compressor surge, reformer carbon deposition, fuel cell thermal crack and turbine blade overheat. Meanwhile, the fuzzy logic theory is introduced to self-tuning the control parameters to meet the control requirements of the nonlinear time varying system. The analysis in system transient behavior during load step changes operation indicates that the present work can realize parameters decoupling and eliminate the instability of SOFC-GT. The proposed control strategy can reduce the SOFC current overshoot by 10.8% during the load step-down operation, meanwhile, this reduces the transient maximum temperature changing rate by almost 47.7%. In addition, by changing the transient behavior of air and fuel flow, the proposed control strategy can reduce the temperature overshoot by 1.16% in load step-up operation. This leads to the transient maximum temperature gradient is decreased by 0.18K/cm.
This study develops and implements a design of the Fuzzy Proportional Integral Derivative with filtered derivative mode (Fuzzy PIDF) for Load Frequency Control (LFC) of a two-area interconnected power system. To attain the optimal values of the proposed structure’s parameters which guarantee the best possible performance, the Bees Algorithm (BA) and other optimisation tools are used to accomplish this task. A Step Load Perturbation (SLP) of 0.2 pu is applied in area one to examine the dynamic performance of the system with the proposed controller employed as the LFC system. The supremacy of Fuzzy PIDF is proven by comparing the results with those of previous studies for the same power system. As the designed controller is required to provide reliable performance, this study is further extended to propose three different fuzzy control configurations that offer higher reliability, namely Fuzzy Cascade PI − PD, Fuzzy PI plus Fuzzy PD, and Fuzzy (PI + PD), optimized by the BA for the LFC for the same dual-area power system. Moreover, an extensive examination of the robustness of these structures towards the parametric uncertainties of the investigated power system, considering thirteen cases, is carried out. The simulation results indicate that the contribution of the BA tuned the proposed fuzzy control structures in alleviating the overshoot, undershoot, and the settling time of the frequency in both areas and the tie-line power oscillations. Based on the obtained results, it is revealed that the lowest drop of the frequency in area one is −0.0414 Hz, which is achieved by the proposed Fuzzy PIDF tuned by the BA. It is also divulged that the proposed techniques, as was evidenced by their performance, offer a good transient response, a considerable capability for disturbance rejection, and an insensitivity towards the parametric uncertainty of the controlled system.
The electrical power system has experienced several changes during the last decade, raised by continuously increasing load demand, rapid depletion in fossil fuels, and newly electrical deregulation policy. In the past, numerous review literatures has been published in the field of Load Frequency Control (LFC), which deals with different and recent control strategies for the successful operation of the power system. Moreover, due to changes in lifestyle, increasing load demand, expansion of industrialization, and environmental issues, Renewable Energy Sources (RES) integration becomes the obvious adaptive choice. Since generation from RES is stochastic in nature and depends upon the weather condition and other aspects at every instant of time. Therefore, high penetration may reflect specific issues regarding voltage instability, frequency stabilization, and reliability obstruction. Hence this issue has been covered effectively in this review work. Furthermore, the work precisely summarized and briefly explained the different scenarios of Energy Storage (ES), micro-grid and Flexible AC Transmission System (FACTS) to explore the possible solutions and future aspects. The merit and demerit of different controllers are also investigated with the help of a comparison's tables and also some other analytical comparisons. The overall study examines an in-depth review of recent technical core aspects for LFC based on classical and modern power system which involves the non-linear model, controller design parameters, soft-computing application, attributes of Load forecasting, as well as integration of RES in a deregulated environment. This effective, comprehensive literature survey is very helpful for researchers to bridge the gap between recent development, implementation, challenges and future trends of RES in LFC.
Modern power system faces excessive frequency aberrations due to the intermittent renewable generations and persistently changing load demands. To avoid any possible blackout, an efficient and robust control strategy is obligatory to minimise deviations in the system frequency and tie-line. Hence, to achieve this target, a new two-degree of freedom-tilted integral derivative with filter (2DOF-TIDN) controller is proposed in this work for a two-area wind-hydro-diesel power system. To enhance the outcome of the proposed 2DOF-TIDN controller, its gain parameters are optimized with the use of a newly designed hybrid bat algorithm-harmony search algorithm (hybrid BA-HSA) technique. The effectiveness and superiority of hybrid BA-HSA tuned 2DOF-TIDN is validated over various existing optimization techniques like cuckoo search (CS), particle swarm optimization (PSO), HSA, BA and teaching learning-based optimization (TLBO). To further refine the system outcome in the dynamic conditions, several flexible AC transmission systems (FACTS) and superconducting magnetic energy storage (SMES) units are adopted for enriching the frequency and tie-line responses. The FACTS controllers like static synchronous series compensator (SSSC), thyristor-controlled phase shifter (TCPS), unified power flow controller (UPFC) and interline power flow controller (IPFC) are employed with SMES simultaneously. The simulation results disclose that the hybrid BA-HSA based 2DOF-TIDN shows superior dynamic performance with IPFC-SMES than other studied approaches. A sensitivity analysis is examined to verify the robustness of proposed controller under ±25% changes in loading and system parameters.
In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm’s local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles’ diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition.
This paper reviews on the function of Automatic Generation Control (AGC) as an intelligent mechanism in enhancing electrical power systems dynamic performance at various perturbations; by sustaining the frequency and tie-line power within the scheduled limits. The review discloses the investigation on the flexible ac transmission system (FACTS), soft computing-based optimization techniques, centralized approaches, decentralized methods, adaptive control approaches, and robust control techniques in AGC systems. Furthermore, the discussion focuses on applications of AGC with energy storage systems (ESS), high voltage direct current (HVDC) link, distributed generation, microgrids, and smart grids, in both traditional and liberalized power systems. Eventually, this paper concludes by highlighting the economic load dispatch methods, load forecasting methods, and digital control approach in AGC systems.
In this manuscript, the load frequency control (LFC) of multiple area hybrid power system (PS) integrated with renewable energy sources (RESs) using energy storage system (ESS) is proposed. The proposed hybrid method is the joint execution of Meta‐heuristic Anopheles Search Algorithm (MASA) and artificial intelligence (AI) technique and hence it is named as MASAAI technique. The main aim of the proposal is “to diminish the load frequency deviations in 3‐area hybrid PS.” The thyristor controlled phase shifter (TCPS) and ultra‐capacitor (UC) application are used in LFC. During the operation of power system, the power distribution quality is suffered owing to continual and random changes in load. Hence, the PS control is necessary for maintaining a continual balance amid the power generation and load demand. The proportional integral derivative (PID) is agreed to LFC mode. Here, the 3‐area system is incorporated with conventional RESs like solar, biomass, and fuel cell. UPFC is joined in series with tie‐line, ensures the stabilization of the system and also presents more service and the quality of power is sustained by the subordinate control of super capacitor energy storage. With the help of MASAAI method, suppressing the peak value of the frequency deviation by power modulation control is a way to lessen this issue. With this proper control, the MASAAI method efficiency becomes optimum in deregulated environment that is agreed to LFC mode. Finally, the proposed method is activated in MATLAB/Simulink site and the performance is compared with existing methods. Finally, the simulation outcomes demonstrate that the efficiency of MASAAI method is more efficient compared to the existing methods.
This paper presents the design and performance analysis of Differential Evolution (DE) algorithm based Proportional-Integral (PI) controller for Automatic Generation Control (AGC) of an interconnected power system. A two area non-reheat thermal system equipped with PI controllers which is widely used in literature is considered for the design and analysis purpose. The design problem is formulated as an optimization problem control and DE is employed to search for optimal controller parameters. Three different objective functions using Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients are derived in order to increase the performance of the controller. The superiority of the proposed DE optimized PI controller has been shown by comparing the results with some recently published modern heuristic optimization techniques such as Bacteria Foraging Optimization Algorithm (BFOA) and Genetic Algorithm (GA) based PI controller for the same interconnected power system.
A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation.
A new heuristic approach for minimizing possiblynonlinear and non-differentiable continuous spacefunctions is presented. By means of an extensivetestbed it is demonstrated that the new methodconverges faster and with more certainty than manyother acclaimed global optimization methods. The newmethod requires few control variables, is robust, easyto use, and lends itself very well to parallelcomputation.
Automatic control has played a vital role in the advancement ofengineering and science. It is also essential in such industrialoperations as controlling pressure, temperature, humidity,viscosity and flow in the process industries. Proportional IntegralDifferential (PID) controllers marked its place in many of theindustrial processes. Tuning a controller is the adjustment of itscontrol parameters. Computational Intelligence (CI) an off shootof Artificial Intelligence relies on heuristic algorithms mainlyevolutionary computation. Swarm intelligence (SI) a derivative ofCI, describes the collective behaviour of decentralized, selforganizedsystems. Ant behaviour was the inspiration for theMeta heuristic optimization technique. This paper presents anapplication of an Ant Colony Optimization (ACO) algorithm tooptimize the parameters in the design of a (PID) controller for ahighly nonlinear conical tank system. The proposed workdiscusses in detail, the ACO, a CI technique, and its applicationover the parameter tuning of a PI controller in a real timeprocess. The designed controller‟s ability in tracking a given setpoint is compared with an Internal Model Control (IMC) tunedcontroller.
In this paper, load frequency control (LFC) of a realistic power system with multi-source power generation is presented. The single area power system includes dynamics of thermal with reheat turbine, hydro and gas power plants. Appropriate generation rate constraints (GRCs) are considered for the thermal and hydro plants. In practice, access to all the state variables of a system is not possible and also their measurement is costly and difficult. Usually only a reduced number of state variables or linear combinations thereof, are available. To resolve this difficulty, optimal output feedback controller which uses only the output state variables is proposed. The performances of the proposed controller are compared with the full state feedback controller. The action of this proposed controller provides satisfactory balance between frequency overshoot and transient oscillations with zero steady state error in the multi-source power system environment. The effect of regulation parameter (R) on the frequency deviation response is examined. The sensitivity analysis reveals that the proposed controller is quite robust and optimum controller gains once set for nominal condition need not to be changed for ±25% variations in the system parameters and operating load condition from their nominal values. To show the effectiveness of the proposed controller on the actual power system, the LFC of hydro power plants operational in KHOZESTAN (a province in southwest of Iran) has also been presented.
This paper presents the MATLAB simulink dynamic model of the load frequency control (LFC) of a realistic two area power system having diverse sources of power generation. The DC link is used in parallel with AC tie line for the interconnection of power system. The power system simulation is done using MATLAB simulink and control problem is solved using MATLAB programming. An optimal output feedback controller with pragmatic viewpoint is presented. Optimal Gain settings of the output feedback controller with and without DC tie line are obtained following a step load disturbance in either area by minimizing the quadratic performance index. The performance of the controller is compared for the power system with and without DC tie line. Simulation results show that the system with AC-DC parallel tie line achieves better performance in the presence of plant parameter changes and system nonlinearities. Further, results show that the output feedback method is the most rational technique with the good dynamic response when the power demands change.
Controllers design problems are multi objective optimization problems as the controller must satisfy several performance measures that are often conflicting and competing with each other. In multi-objective approach a set of solutions can be generated from which the designer can select a final solution according to his requirement and need. This paper presents the design and analysis Proportional Integral (PI) and Proportional Integral Derivative (PID) controller employing multi-objective Non-Dominated Shorting Genetic Algorithm-II (NSGA-II) technique for Automatic Generation Control (AGC) of an interconnected system. To minimize the effect of noise in the input signal, a filter is employed with the derivative term. Integral Time multiply Absolute Error (ITAE), minimum damping ratio of dominant eigenvalues and settling times in frequency and tie-line power deviations are considered as multiple objectives and NSGA-II is employed to generate Pareto optimal set. Further, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto solution set. The proposed approach is first applied to a linear two-area power system model and then extended to a non-linear power system model by considering the effect of governor dead band non-linearity. The superiority of the proposed NSGA-II optimized PI/PID controllers has been shown by comparing the results with some recently published modern heuristic optimization approaches such as Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA) and Craziness based Particle Swarm Optimization (CPSO) based controllers for the similar interconnected power systems.
In this paper coordination scheme to improve the stability of a power system by optimal design of power system stabilizer and static synchronous series compensator (SSSC)-based controller is presented. Time delays owing to sensor and signal transmission delays are included in the design. The coordinated design problem is formulated as an optimization problem and hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA–PSO) is employed to search for the optimal controller parameters. The performance of the proposed controllers is evaluated for both single-machine infinite-bus power system and multi-machine power system. Results are presented over a wide range of loading conditions and system configurations to show the effectiveness and robustness of the proposed coordinated design approach. It is observed that the proposed controllers provide efficient damping to power system oscillations under a wide range of operating conditions and under various disturbances. Further, simulation results show that, in a multi-machine power system, the modal oscillations are effectively damped by the proposed approach.
In this paper, design and performance analysis of Differential Evolution (DE) algorithm based parallel 2-Degree Freedom of Proportional-Integral-Derivative (2-DOF PID) controller for Load Frequency Control (LFC) of interconnected power system is presented. A two area thermal system with governor dead-band nonlinearity is considered for the design and analysis purpose. The design problem is formulated as an optimization problem and DE is employed to search for optimal controller parameters. Conventional and modified objective functions are used for the design purpose. Conventional objective functions employed in the paper are Integral of Time multiplied by Squared Error (ITSE) and Integral of Squared Error (ISE). In order to further increase the performance of the controller, a modified objective function is derived using Integral Time multiply Absolute Error (ITAE), damping ratio of dominant eigenvalues, settling times of frequency and peak overshoots with appropriate weight coefficients. The superiority of the proposed approach has been demonstrated by comparing the results with a recently published technique, i.e. Craziness based Particle Swarm Optimization (CPSO) for the same interconnected power system. Further, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the proposed controllers are quite robust for a wide range of the system parameters and operating load conditions from their nominal values. Finally, the proposed approach is extended to a more realistic power system model by considering the physical constraints such as time delay, reheat turbine, Generation Rate Constraint (GRC) and governor dead band.
Selective harmonic elimination-pulse width modulation techniques offer a tight control of the harmonic spectrum of a given voltage waveform generated by a power electronic converter along with a low number of switching transitions. Traditional optimization methods suffer from various drawbacks, such as prolonged and tedious computational steps and convergence to local optima; thus, the more the number of harmonics to be eliminated, the larger the computational complexity and time. This paper presents a novel method for output voltage harmonic elimination and voltage control of PWM AC/AC voltage converters using the principle of hybrid Real-Coded Genetic Algorithm-Pattern Search (RGA-PS) method. RGA is the primary optimizer exploiting its global search capabilities, PS is then employed to fine tune the best solution provided by RGA in each evolution. The proposed method enables linear control of the fundamental component of the output voltage and complete elimination of its harmonic contents up to a specified order. Theoretical studies have been carried out to show the effectiveness and robustness of the proposed method of selective harmonic elimination. Theoretical results are validated through simulation studies using PSIM software package. Finally, these results are verified by means of an experimental prototype.
This paper deals with automatic generation control (AGC) of a three unequal area hydrothermal system. Reheat turbines in thermal areas and electric governor in hydro area are considered. Appropriate generation rate constraints are considered in the areas. Bacterial foraging (BF) technique is used to simultaneously optimize the integral gains (KIi) and speed regulation parameter (Ri) keeping frequency bias fixed at frequency response characteristics. The integral controller in this case is termed as BFIC. The performance of a multilayer perception neural network (MLPNN) controller using reinforcement learning is evaluated for the system. In this reinforcement learning, the weights are dynamically adjusted online using backpropagation algorithm with error being the area control error (ACE). The performance of the MLPNN controller is compared with that of BFIC. Also, the performance of MLPNN controller over a wide range of system loading conditions and step load perturbations is compared with BFIC. Investigations clearly reveal the superior performance of MLPNN controller over BFIC. Sensitivity analysis subject to wide changes in system loading, inertia constant (H) and size and location of step load perturbation is carried out to investigate the robustness of the controller with the optimum KIi and Ri obtained at nominal condition.
Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real world optimization problems. This paper proposes BFOA based Load Frequency Control (LFC) for the suppression of oscillations in power system. A two area non-reheat thermal system is considered to be equipped with proportional plus integral (PI) controllers. BFOA is employed to search for optimal controller parameters by minimizing the time domain objective function. The performance of the proposed controller has been evaluated with the performance of the conventional PI controller and PI controller tuned by genetic algorithm (GA) in order to demonstrate the superior efficiency of the proposed BFOA in tuning PI controller. Simulation results emphasis on the better performance of the optimized PI controller based on BFOA in compare to optimized PI controller based on GA and conventional one over wide range of operating conditions, and system parameters variations.
This book discusses the automatic closed-loop control of generators, which is the key to the successful operation of modern power plants and power systems. The first edition of this book gained wide readership because of its control topics and its coverage of optimum operation and load flow analysis. Now these topics have been greatly expanded to conform with the state-of-the-art of electric energy systems, and a new and unique emphasis has been placed on emergency control. The mechanism that transforms mechanical power into electrical megawatts in three-phase synchronous machines, and the collective behavior of the many machines that make up a modern power system are presented. A detailed treatment of the Newton-Raphson powerflow algorithm, an overview of system protection, examples in unbalanced fault analysis, a step-by-step example of a building-algorithm, explanations of subharmonic resonance and intermachine oscillations, turbine transfer functions, utilization of the state transition diagram and a chapter on three-phase theory are offered.
A new PID controller for resistant differential control against load disturbance is introduced that can be used for load frequency control (LFC) application. Parameters of the controller have been specified by using imperialist competitive algorithm (ICA). Load disturbance, which is due to continuous and rapid changes of small loads, is always a problem for load frequency control of power systems. This paper introduces a new method to overcome this problem that is based on filtering technique which eliminates the effect of this kind of disturbance. The object is frequency regulation in each area of the power system and decreasing of power transfer between control areas, so the parameters of the proposed controller have been specified in a wide range of load changes by means of ICA to achieve the best dynamic response of frequency. To evaluate the effectiveness of the proposed controller, a three-area power system is simulated in MATLAB/SIMULINK. Each area has different generation units, so utilizes controllers with different parameters. Finally a comparison between the proposed controller and two other prevalent PI controllers, optimized by GA and Neural Networks, has been done which represents advantages of this controller over others.
An internal model control (IMC) based tuning method is proposed to autotune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which consists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a process disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control system is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method.
A maiden attempt is made to examine and highlight the effective application of bacterial foraging (BF) to optimize several important parameters in automatic generation control (AGC) of interconnected three unequal area thermal systems, such as integral controller gains (KIi) for the secondary control, governor speed regulation parameters (Ri) for the primary control and frequency bias parameters (Bi), and compare its performance to establish its superiority over genetic algorithm (GA) and classical methods. Comparison of convergence characteristics of BF, GA, and classical approach reveals that the BF algorithm is quite faster in optimization, leading to reduction in computational burden and giving rise to minimal computer resource utilization. Simultaneous optimization of KIi, Ri, and Bi parameters which surprisingly has never been attempted in the past, provides not only best dynamic response for the system but also allows use of much higher values of Ri (than used in practice), that will appeal to the power industries for easier and cheaper realization of governor. Sensitivity analysis is carried out which demonstrates the robustness of the optimized KIi, Ri, and Bi to wide changes in inertia constant (H), reheat time constant (Tr), reheat coefficient (Kr), system loading condition, and size and position of step load perturbation.
In this study, a novel gain scheduling Proportional-plus-Integral (PI) control strategy is suggested for automatic generation control (AGC) of the two area thermal power system with governor dead-band nonlinearity. In this strategy, the control is evaluated as an optimization problem, and two different cost functions with tuned weight coefficients are derived in order to increase the performance of convergence to the global optima. One of the cost functions is derived through the frequency deviations of the control areas and tie-line power changes. On the other hand, the other one includes the rate of changes which can be variable depends on the time in these deviations. These weight coefficients of the cost functions are also optimized as the controller gains have been done. The craziness based particle swarm optimization (CRAZYPSO) algorithm is preferred to optimize the parameters, because of convergence superiority. At the end of the study, the performance of the control system is compared with the performance which is obtained with classical integral of the squared error (ISE) and the integral of time weighted squared error (ITSE) cost functions through transient response analysis method. The results show that the obtained optimal PI-controller improves the dynamic performance of the power system as expected as mentioned in literature.
By relating to the conventional PID control theory, we propose a new fuzzy controller structure, namely PID type fuzzy controller. In order to improve further the performance of the transient state and the steady state of the PID type controller, we develop a method to tune the scaling factors of the PID type fuzzy controller on line. Simulation of the PID type fuzzy controller with the self-tuning scaling factors shows a better performance in the transient and steady state response.
A robust self-tuning PI-type fuzzy logic controller (FLC) is presented. Depending on the process trend, the output scaling factor (SF) of the controller is modified on-line by an updating factor (α). The value of α is determined from a rule-base defined on error (e) and change of error (Δe) of the controlled variable. The proposed self-tuning controller is designed using a very simple control rule-base and the most natural and unbiased membership functions (MFs) (symmetric triangles with equal base and 50% overlap with neighboring MFs). The proposed scheme is tested for a wide variety of processes including a marginally stable system with different values of dead time. Performance comparison between the conventional PI-type and proposed self-tuning FLCs is made in terms of several performance criteria such as peak overshoot, settling time, rise time, integral absolute error and integral-of-time-multiplied absolute error, in addition to the responses due to step input and load disturbance. Results for various processes show that the proposed FLC outperforms its conventional counterpart in each case.
In this paper, a self tuning fuzzy PID type controller is proposed for solving the load frequency control (LFC) problem. The fuzzy PID type controller is constructed as a set of control rules, and the control signal is directly deduced from the knowledge base and the fuzzy inference. Moreover, there exists a self tuning mechanism that adjusts the input scaling factor corresponding to the derivative coefficient and the output scaling factor corresponding to the integral coefficient of the PID type fuzzy logic controller in an on-line manner. The self tuning mechanism depends on the peak observer idea, and this idea is modified and adapted to the LFC problem. A two area interconnected system is assumed for demonstrations. The proposed self tuning fuzzy PID type controller has been compared with the fuzzy PID type controller without a self tuning mechanism and the conventional integral controller through some performance indices.
We examine the local convergence properties of pattern search methods, comple- menting the previously established global convergence properties for this class of algorithms. We show that the step-length control parameter which appears in the definition of pattern search al- gorithms provides a reliable asymptotic measure of first-order stationarity. This gives an analytical justification for a traditional stopping criterion for pattern search methods. Using this measure of first-order stationarity, we both revisit the global convergence properties of pattern search and analyze the behavior of pattern search in the neighborhood of an isolated local minimizer.
A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input–output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PIλDμ controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.
Frequency control as a major function of automatic generation control is one of the important control problems in electric power system design and operation, and is becoming more significant today due to the increasing size, changing structure, emerging new uncertainties, environmental constraints, and the complexity of power systems. Robust Power System Frequency Control uses the recent development of linear robust control theory to provide practical, systematic, fast, and flexible algorithms for the tuning of power system load-frequency controllers.
The physical constraints and important challenges related to the frequency regulation issue in a deregulated environment are emphasized, and most results are supplemented by real-time simulations. The developed control strategies attempt to bridge the existing gap between the advantages of robust/optimal control and traditional power system frequency control design.
The material summarizes the long term research outcomes and contributions of the author’s experience with power system frequency regulation. It provides a thorough understanding of the basic principles of power system frequency behavior over a wide range of operating conditions. It uses simple frequency response models, control structures and mathematical algorithms to adapt modern robust control theorems with frequency control issues as well as conceptual explanations. The engineering aspects of frequency regulation have been considered, and practical methods for computer analysis and design are also discussed.
Robust Power System Frequency Control provides a comprehensive coverage of frequency control understanding, simulation and design. The material develops an appropriate intuition relative to the robust load frequency regulation problem in real-world power systems, rather than to describe sophisticated mathematical analytical methods.
Several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, we evaluate the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.
Conventional fuzzy control can be considered mainly composed of
fuzzy two-term control and fuzzy three-term control. In this paper, more
systematic analysis and design are given for the conventional fuzzy
control. A general robust rule base is proposed for fuzzy two-term
control, leaving the optimum tuning to the scaling gains, which greatly
reduces the difficulties of design and tuning. The digital
implementation of fuzzy control is also presented for avoiding the
influence of the sampling time. Based on the results of previous fuzzy
two-term controllers, a simplified fuzzy three-term controller is
proposed to enhance performance. A two-level tuning strategy is also
planned, which first tries to set up the relationship between fuzzy
proportional/integral/derivative gain and scaling gains at the high
level, and optionally tunes the control resolution at low level.
Simulation of different order models show the characteristics of fuzzy
control, effectiveness of the new design methodologies, and advantages
of the enhanced fuzzy three-term control
The paper presents a comparison between different methods, based
on fuzzy logic, for the tuning of PID controllers. Specifically
considered are different control structures in which a fuzzy mechanism
is adopted to improve the performances given by Ziegler-Nichols
parameters. To verify the full capabilities of each controller, genetic
algorithms are used to tune the parameters of the fuzzy inference
systems (scaling coefficients, shape of the membership functions, etc.).
Furthermore, a discussion about the practical implementation issue of
the controllers is provided, and comparisons made with a typical
PID-like fuzzy controller and a standard nonlinear PID controller. The
results show the superiority of the fuzzy set-point weighting
methodology over the other methods
Proposes a simple but robust model independent self-tuning scheme
for fuzzy logic controllers (FLCs). Here, the output scaling factor (SF)
is adjusted online by fuzzy rules according to the current trend of the
controlled process. The rule-base for tuning the output SF is defined on
error (e) and change of error (Δe) of the controlled variable
using the most natural and unbiased membership functions (MFs). The
proposed self-tuning technique is applied to both PI- and PD-type FLCs
to conduct simulation analysis for a wide range of different linear and
nonlinear second-order processes including a marginally stable system
where even the well known Ziegler-Nichols tuned conventional PI or PID
controllers fail to provide an acceptable performance due to excessively
large overshoot. Performances of the proposed self-tuning FLCs are
compared with those of their corresponding conventional FLCs in terms of
several performance measures such as peak overshoot, settling time, rise
time, integral absolute error and integral-of-time-multiplied absolute
error, in addition to the responses due to step set-point change and
load disturbance and, in each case, the proposed scheme shows a
remarkably improved performance over its conventional counterpart
This paper deals with automatic generation control of an interconnected hydrothermal system in continuous-discrete mode using conventional integral and proportional-integral controllers. Appropriate generation rate constraint has been considered for the thermal and hydro plants. The hydro area is considered with either mechanical or electric governor and thermal area is considered with either single or double reheat turbine. Performances of mechanical governor, electric governor, and single stage reheat turbine and two stage reheat turbine on dynamic responses have been explored. Further, selection of suitable value of speed regulation parameter R and sampling period has been investigated. System performance is examined considering 1% step load perturbation in either thermal or hydro area.
An attempt is made in This work to present critical literature review and an up-to-date and exhaustive bibliography on the AGC of power systems. Various control aspects concerning the AGC problem have been highlighted. AGC schemes based on parameters, such as linear and nonlinear power system models, classical and optimal control, and centralized, decentralized, and multilevel control, are discussed. AGC strategies based on digital, self-tuning control, adaptive, VSS systems, and intelligent/soft computing control have been included. Finally, the investigations on AGC systems incorporating BES/SMES, wind turbines, FACTS devices, and PV systems have also been discussed.