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Maximizing the overall production of wind farms by setting the individual operating point of wind turbines

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Abstract

The classical operation strategy of wind farms seeks each wind turbine to convert as much aerodynamic power as available from the incoming airflow. But this does not warranty that the power converted by the whole wind farm be a maximum due to the interaction between turbines (wake effect). Unlike the conventional operation, this paper proposes the individual selection of the operation point of each turbine so that the overall production of the wind farm is maximized. To reach that goal, the power produced by some upwind turbines is slightly reduced in order to increase the available aerodynamic power for the downwind turbines, which results in an increase of the overall wind farm energy extraction. The optimization is performed by means of a genetic algorithm that selects the optimal pitch angle and tip speed ratio of each individual wind turbine, in order to maximize the overall wind farm production.

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... They demonstrated that optimizing the pitch variation of the turbines increases the power generated by Horns Rev by 4.5%. These results were improved by Serrano-González et al. [28]. Similarly to [27], they used the layout configuration of the Horns Rev wind farm as a reference. ...
... The objective of the optimization is to maximize the overall power produced by the tidal farm. Thus, the optimization problem can be written as [28]: ...
... As both discrete and continuous variables are involved in the calculation of the wake effect, we deal with an integer-mixed problem. Thus, the classic analytical optimization techniques cannot be applied (because the problem is non-derivable [28,54]), and meta-heuristics optimization methods are preferred. ...
Article
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In the next years, tidal farm will increase in size and density of devices to obtain an economically significant amount of energy. Because of the high density of the devices within the farm, the turbines will be hydrodynamically coupled. The negative effects of this coupling could be reduced by optimizing the tidal farm layout or by optimizing the operating point of each turbine within the farm. In this paper, we investigate the second strategy. The objective is to maximize the overall production of the farm. The power of the upstream tidal turbines is then reduced, allowing the increase of power of the downstream turbines. The Binary Particle Swarm Optimization (BPSO) Algorithm is used in order to find the rotational speed for each device such that the net energy yield is optimized over a tidal cycle. The proposed methodology is firstly applied to an ideal case (i.e. tidal flow constant in magnitude and direction) to assess the influence of the machines density, the ambient turbulence rate and the speed magnitude on the power improvement rate. Secondly, the method is applied to a hypothetical tidal farm located in the Alderney Race (Raz Blanchard in French), situated between the Alderney Island and La Hague Cape (France). The results of these scenarios indicate that the proposed strategy is interesting mainly when the density of devices is high and/or when the ambient turbulence rate is lower than 10%. In this cases, it is possible to improve the rate by 2.5%.
... The methods were validated through a simple array layout wind farm and the power production estimation was accomplished by blade element momentum (BEM) theory and eddy viscosity model (EVM). In the meantime, a simultaneous optimization method of tip speed ratio and blade pitch angle was specified in [62] and validated using Horns Rev I wind farm layout. Similarly, the pitch angle optimization for maximizing the overall power production of the wind farm was done in [63] with a simplified wake model. ...
... Recently, an optimized power dispatch strategy for a scatter wind farm layout was proposed with the purpose of minimizing the levelized production cost [64]. Compared with [61][62][63][64] where metaheuristic optimization method was adopted to benefit the objective function, a gradient-based optimization has been reported in [65] to improve the power production of the offshore wind farm. ...
... As mentioned in the previous text, the estimation of energy production of an offshore wind farm highly depends on the wake model and control strategy. Though many works [61][62][63][64][65] have presented the possibility of increasing the energy yields of whole wind farm using new control strategy, there is no evidence for its application in real farms. Hence, the WFLOP is always done based on assuming a MPPT control strategy. ...
Article
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There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. Since a huge amount of money is required for constructing an offshore wind farm, many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production. There are several factors that have an impact on the performance of the wind farm, mainly energy production of wind farm which is highly decided by the wind condition of construction area and micro-siting of wind turbines (WTs), as well as initial investment which is influenced by both the placement of WTs and the electrical system design, especially the scheme of cable connection layout. In this paper, a review of the state-of-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented. The most significant factors that should be considered in the offshore wind farm optimization work is highlighted after reviewing the latest works, and the future needs have been specified.
... This report showed optimal topology design and control of WF system to enhance the overall efficiency of the WF system by adjusting the pitch angle as well as the tip speed ratio. However, the detailed model and result analysis were not presented [14]. The detailed wake model and optimization model have been presented using different optimization methods, such as genetic algorithm [14]- [16] and Adam optimization [17]. ...
... However, the detailed model and result analysis were not presented [14]. The detailed wake model and optimization model have been presented using different optimization methods, such as genetic algorithm [14]- [16] and Adam optimization [17]. The authors in [14] and [15] have developed an optimization model using genetic algorithm to determine the optimal operation point (i.e. ...
... The detailed wake model and optimization model have been presented using different optimization methods, such as genetic algorithm [14]- [16] and Adam optimization [17]. The authors in [14] and [15] have developed an optimization model using genetic algorithm to determine the optimal operation point (i.e. tip speed ratio and pitch angle) of each WTG for maximizing the overall WF production. ...
Article
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In the conventional operation of a wind farm (WF) system, the operation point of each wind turbine generator (WTG) is determined to capture maximum energy individually using maximum power point tracking (MPPT) algorithm. However, this operation strategy might not ensure the maximum output power of WF due to wake effect among WTGs. Therefore, this paper develops a multi-agent-based cooperative learning strategy among WTGs using deep reinforcement learning to enhance the overall efficiency of WF by minimizing the wake effect. WTG agents are learnable units and they interact with others as an extensive-form game based on a cooperative model to achieve a common goals (i.e. maximum output power of the WF). In this game, WTG agents carry out their actions sequentially and measure a common reward which is used to update the knowledge of all agents. During the training process, WTG agents use different deep neural networks (DNNs) to improve their actions for achieving the higher reward in the long run by optimizing the weights of DNNs in each learning step. After the training process, WTG agents are able to determine optimal set-points with different input information to minimize the wake effect and to maximize the output power of the WF. Moreover, an operation strategy for the entire WF system is proposed to ensure that the WF always complies with grid-code constraints from transmission system operators, including the requirement of limited power and reserve power. In order to show the effectiveness of the proposed method, a comparison between the results using the proposed method and the conventional MPPT method is also presented in different cases, and the results show that the proposed method can increase the output power of the WF in the range of 1.99% to 4.11% with different layouts.
... The main objective operation is to minimize the sum of production cost, the unserved energy cost, and start-up/shut-down cost of each WTG. The authors in [18] have proposed the individual selection of the operation point of each WTG to maximize the overall production of the WF system. Similarly, the authors in [19] have also presented an optimization method to calculate the optimal operation of an offshore WF working with WTGs based on doubly fed induction generator technologies. ...
... The objective of the optimization problem is to maximize the output of active power of the WF system. Most of these mentioned studies focused either on maximizing the output power of the WF system [18]- [20], minimizing the operation cost [15], [21], or minimizing the amount of power mismatch between generated power and required power [15], [17]. It can be observed that these operation objectives mainly focus on optimizing the operation of the WF system. ...
... the event time Nh is the number of WTGs are out of service at time h Ne = N-Nh is the number of WTGs in service Power mismatch and power loss are calculated similarly by(2),(3)and(4), respectively. Depending on the type of event, the amount of required power is changed as shown in(18). ...
Article
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In wind farm (WF) systems, the set-points of wind turbine generators (WTGs) is determined by the wind farm operator. A change in the value of set-point in two consecutive intervals leads to power fluctuations in the WF system, i.e. power deviation. A large amount of power deviation may adversely affect the operation of the WF system. Therefore, this paper proposes a strategy for operation of a WF system to minimize the power deviation of WTGs during operation time. By using the proposed strategy, the change in set-points of WTGs is minimized and the output power of WTGs is therefore smoother and avoids unnecessary fluctuations. Several grid-code constraints are also considered for operation of the WF system. This ensures the WF system to operate in compliance with the requirements from transmission system operator. Besides, an additional strategy is also proposed to monitor all events in the WF system. Whenever an event occurs in the system, the set-points of WTGs are rescheduled considering the event. Therefore, the proposed operation strategy is also capable of handling events in the WF system. Finally, the simulation results with and without the proposed method are compared to show the effectiveness of the proposed method.
... Proposing a methodology that considered optimizing only the pitch setting of each turbine by using a genetic algorithm (GA) to navigate through the design space, it was shown in [5] that proper pitch angle selection could in fact improve overall farm performance. Furthermore, based off the work of Lee [5], an optimization procedure founded on a two-parameter design space for a given wind speed and wind direction was further developed in [6] that improved the global AEP by 1.56 % when applied onto an 80-WT wind farm in a rectangular grid arrangement compared to normal operation settings (variable speed configuration). All previous studies are based on simple engineering wake models, and most of these operate with only one design variable per WT. ...
... All previous studies are based on simple engineering wake models, and most of these operate with only one design variable per WT. An exception is [6], which operates with the same two design parameters per WT as used in the present study. However, contrary to the this study, the present study is based on a consistent CFD based flow model to resolve the complex wind field inside a WPP, and further a consistent collapse of the two-parameter design space to a one-parameter design space per WT is introduced for improved computational performance. ...
Article
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This paper presents a general purpose platform for optimal open loop control of wind power plants as seen from a power production perspective. The general idea is to change the controller design criteria from greedy individual wind turbines to a controller design facilitating cooperative and interdependent elements of a wind power plant, with the overall aim to improve the wind power plant power production conditioned on ambient mean wind speed and mean wind direction. The flow within the wind power plant, including all essential interactions between the wind turbines, is modelled using a very fast linearized CFD RANS solver. The wind turbines are modelled as actuator discs, and two design variables per wind turbine – collective pitch, α , and tip speed ratio, λ – are initially defined for the optimization problem. However, a priory we expect one design variable to suffice – i.e. the unique set of (α, λ) representing the lowest thrust coefficient, C T , for a given power coefficient C p . The conjectured collapse of the design space is justified in this paper. Optimized control schemes for the Lillgrund offshore wind farm are derived conditioned on ambient mean wind direction and wind speed. Aggregated over a year, using the site sector Weibull distributions, an increase in the annual energy production of 1% is demonstrated.
... The Jensen model has widely been used for developing farm control strategies due to its processing efficiency [2,4,7,8,[39][40][41][42] and is also part of many industry standard software such as WindFarmer [7] and WindPRO [31]. Simple assumptions such as the ideal wind flow, constant k and linear wake expansion make the Jensen model computationally very efficient. ...
... Wind farm coordinated control is a complex optimisation problem as each individual turbine's production is a dimension of the farm production [40]. Numerical optimisation [1,10,[44][45][46][47][48][49][50][51], game-theoretic approach [39,42,52], hill climbing algorithm [53] and genetic algorithm [41,54] are some of the techniques used for solving the coordinated control problem. However, It is suggested in [3,9] that iterative algorithms can improve performance of farm controllers. ...
Article
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A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines particle swarm optimisation (PSO) with a turbulence intensity–based Jensen wake model (TI–JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ( C P ) or deflecting wakes by applying yaw-offsets for maximising net farm production. Firstly, TI–JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimised strategies are evaluated using simulations based on TI–JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 s for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.
... By developing new active power controllers in [7], using the boundary layer tunnel experiments in [8], and analyzing the wind turbine load in [9], the authors concluded that the power loss in the wind farm due to the wake effect can be reduced by optimizing the pitch angle and tip speed ratio of each wind turbine. In [10,11], optimal pitch angle and tip speed ratio of all the wind turbines are selected at the same time by the optimization algorithms to maximize the total active power of the wind farm, where the wind speed of each wind turbine is estimated by the wake model with the ambient wind speed. The optimized pitch angle and tip speed ratio of each wind turbine are supposed to be implemented in the wind farm central controller. ...
... In this paper, firstly, the optimal pitch angle and tip speed ratio of each wind turbine to maximize the total active power of the wind farm are selected by the exhausted search method. Compared with the particle swarm optimization (PSO)-and genetic algorithm (GA)-based optimization methods as adopted in [10,11], the advantage of by the exhausted search method is that the total active powers of the wind farm at all sets of the pitch angle and tip speed ratio of all the wind turbines are calculated. By the comparison of the total active power of the wind farm at all sets of the pitch angle and tip speed ratio of all the wind turbines, the implementation of the optimized pitch angle and tip speed ratio can be simplified. ...
Article
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In modern wind farms, maximum power point tracking (MPPT) is widely implemented. Using the MPPT method, each individual wind turbine is controlled by its pitch angle and tip speed ratio to generate the maximum active power. In a wind farm, the upstream wind turbine may cause power loss to its downstream wind turbines due to the wake effect. According to the wake model, downstream power loss is also determined by the pitch angle and tip speed ratio of the upstream wind turbine. By optimizing the pitch angle and tip speed ratio of each wind turbine, the total active power of the wind farm can be increased. In this paper, the optimal pitch angle and tip speed ratio are selected for each wind turbine by the exhausted search. Considering the estimation error of the wake model, a solution to implement the optimized pitch angle and tip speed ratio is proposed, which is to generate the optimal control curves for each individual wind turbine off-line. In typical wind farms with regular layout, based on the detailed analysis of the influence of pitch angle and tip speed ratio on the total active power of the wind farm by the exhausted search, the optimization is simplified with the reduced computation complexity. By using the optimized control curves, the annual energy production (AEP) is increased by 1.03% compared to using the MPPT method in a case-study of a typical eighty-turbine wind farm.
... Another reason that so many studies may be inadequate indicators of AIC's potential is that they test it with a single column of turbines aligned to the inflow. 26,33,35,[37][38][39][40]42,48,50,55,56,59,62,63,65,66,[68][69][70]74,76,77,[79][80][81][82]85,89,91,92,94 While such an arrangement does provide a worst-case scenario as a baseline, it may also be a worst-case scenario for AIC because downstream turbines are not optimally placed to harvest the excess energy left by upstream-derated turbines. ...
... 78 They also indicate that power gains will be larger from AIC when turbines are aligned with the flow as opposed to staggered, which is the same as looking at differences in wind direction. 78,82 That gains could be higher when turbines are aligned with the wind may sound contrary to criticisms above regarding testing AIC with a column of turbines, but, in an array, there are turbines to the sides that can harvest the available energy in the wakes of upstream turbines given enough downstream distance for the wakes to expand and mix with each other and the ambient flow. In an aligned array, AIC may show large power gains relative to a worst-case baseline. ...
Article
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The progression of wind turbine technology has led to wind turbines being incredibly optimized machines often approaching their theoretical maximum production capabilities. When placed together in arrays to make wind farms, however, they are subject to wake interference that greatly reduces downstream turbines' power production, increases structural loading and maintenance, reduces their lifetimes, and ultimately increases the levelized cost of energy. Development of techniques to manage wakes and operate larger and larger arrays of turbines more efficiently is now a crucial field of research. Herein, four wake management techniques in various states of development are reviewed. These include axial induction control, wake steering, the latter two combined, and active wake control. Each of these is reviewed in terms of its control strategies and use for power maximization, load reduction, and ancillary services. By evaluating existing research, several directions for future research are suggested.
... The wake effect is one of the causes of WF power loss. Many researchers have focused on how to reduce the wake effect to improve the power generation [3][4][5]. This concept was named Active Wake Control (AWC) in [6]. ...
... Two different methods are commonly used. One method is axial induction control, which is more widely used [4,5]. In general, axial induction control achieves the control objectives by adjusting the Tip Speed Ratio (TSR) and the pitch angle of the WT blades. ...
Article
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For offshore wind farms, the power loss caused by the wake effect is large due to the large capacity of the wind turbine. At the same time, the operating environment of the offshore wind farm is very harsh, and the cost of maintenance is higher than that of the onshore wind farm. Therefore, it is worthwhile to study through reasonable control how to reduce the wake loss of the wind farm and minimize the losses caused by the fault. In this paper, the Particle Swarm Optimization (PSO) algorithm is used to optimize the active power dispatch of wind farms under generator cooling system faults. The optimization objectives include avoiding the further deterioration of the generator fault, reducing unnecessary power loss of the faulty wind turbine, tracking the power demand from the Transmission System Operator (TSO), and reducing the power fluctuation caused by the PSO algorithm. The proposed optimal power dispatch strategy was compared with the two generally-used fault-handling methods and the proportional dispatch strategy in simulation. The result shows that the proposed strategy can improve the power generation capacity of the wind farm and achieve an efficient trade-off between power generation and fault protection.
... A Genetic Algorithm (GA) is used to optimise farm production based on the Park model [20]. This heuristic approach provides computational efficiencies to find good solutions to large complex problems at a small cost of not guaranteeing the most optimal solution. ...
Article
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This paper presents, with a live field experiment, the potential of increasing wind farm power generation by optimally yawing upstream wind turbine for reducing wake effects as a part of the SmartEOLE project. Two 2MW turbines from the Le Sole de Moulin Vieux (SMV) wind farm are used for this purpose. The upstream turbine (SMV6) is operated with a yaw offset ( α ) in a range of − 12 ° to 8° for analysing the impact on the downstream turbine (SMV5). Simulations are performed with intelligent control strategies for estimating optimum α settings. Simulations show that optimal α can increase net production of the two turbines by more than 5%. The impact of α on SMV6 is quantified using the data obtained during the experiment. A comparison of the data obtained during the experiment is carried out with data obtained during normal operations in similar wind conditions. This comparison show that an optimum or near-optimum α increases net production by more than 5% in wake affected wind conditions, which is in confirmation with the simulated results.
... Park and Law et al. [6] studied control strategies for wake effects mitigation, showing that control techniques can be applied for each individual rotor to improve overall wind farm efficiency. González et al. [7] proposed the individual selection of an operating point on each wind turbine in order to maximize the overall wind farm output power. This is performed by studying the optimal pitch angle and Tip Speed Ratio (TSR) of each rotor in regards to the total wind farm output power. ...
Article
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The first part of this work describes the validation of a wind turbine farm Computational Fluid Dynamics (CFD) simulation using literature velocity wake data from the MEXICO (Model Experiments in Controlled Conditions) experiment. The work is intended to establish a computational framework from which to investigate wind farm layout, seeking to validate the simulation and identify parameters influencing the wake. A CFD model was designed to mimic the MEXICO rotor experimental conditions and simulate new operating conditions with regards to tip speed ratio and pitch angle. The validation showed that the computational results qualitatively agree with the experimental data. Considering the designed tip speed ratio (TSR) of 6.6, the deficit of velocity in the wake remains at rate of approximately 15% of the free-stream velocity per rotor diameter regardless of the free-stream velocity applied. Moreover, analysis of a radial traverse right behind the rotor showed an increase of 20% in the velocity deficit as the TSR varied from TSR = 6 to TSR = 10, corresponding to an increase ratio of approximately 5% m·s −1 per dimensionless unit of TSR. We conclude that the near wake characteristics of a wind turbine are strongly influenced by the TSR and the pitch angle.
... To study the foundation damping and dynamics of OWT monopiles, focusing on the foundation damping analysis, Carswell et al. used the logarithmic decay method of the time interval of free vibration to quantify the critical damping percentage caused by foundation damping in the NREL-5 MW model OWT and compared it with the existing experimental and numerical results [6]. To investigate the wind characteristics and wind energy potential of the three remote islands around Hong Kong, Shu et al. used the Weibull distribution function to estimate the Weibull parameters [7,8]. ...
Article
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Flexible multi-body dynamics of wind turbines is a subfield of structural mechanics that mainly studies the response of the coupling structure under dynamic loading, such as the transient changes of displacement and stress, in order to measure the load carrying capacity of the coupling structure and obtain the corresponding dynamic properties. Structural dynamics takes into account not only the damping and inertia forces generated by the vibration of the structure but also the elastic force generated by the deformation of the structure. With the continuous increase of individual power and tower height, the flexibility of the multi-body system of wind turbines also increases. The study of the influence of structural parameters on the coupled structural vibrations of tower blades of large wind turbines can provide a scientific basis for the flexible design of large wind turbines as well as important theoretical support for their safe, stable, and economic operation.
... The resolution of most commercial-scale sensors are usually 0.1m/s[18], which is suitable for reaching CFD validation requirements. Alternatively, other wind measurements devices could be utilized such as Particle Image Velocimetry (PIV). ...
... After the research problem was assessed and the study goal was defined, searches were done in the Thesis Virtual Library using the terms "performance measurement systems", "wind energy" and "wind power", and "wind farm operation & maintenance". The reading of the articles found allowed for the definition of the classic [11] Wind Data envelopment analysis Management inefficiency [12] Wind Data envelopment analysis Consideration of local and social aspects in the choice of site [13] Wind Data envelopment analysis Learning Economy of scale [14] Wind Data envelopment analysis Life cycle analysis Consideration of local and social aspects in the choice of site Organization of work activities [15] Wind [20] Wind Performance measurement system (Dashboard) Sector policies Bottlenecks in the value chain Lack of specific measurement tools that consider managerial and environmental aspects [21] Wind Comparison between different methods Use of appropriate tools to measure performance [22] Wind Monte Carlo simulation Predictive maintenance [23] Wind Genetic algorithms Turbine configuration [24] Wind Reliability thresholds Use of the appropriate maintenance strategy Efficiency in the decision-making process [25] Wind Stochastic programming model Use of the appropriate maintenance strategy Resource allocation [26] Coal Data envelopment analysis Management inefficiency Operating environment [27] Geothermal Performance measurement system Preventive maintenance Use of appropriate performance indicators Quality of records Diffusion of performance measurement culture [28] Geothermal Performance measurement system Use of appropriate performance indicators Resource allocation time management [29] Hydroelectric Data envelopment analysis Management inefficiency Use of resources [30] Hydroelectric Analytic hierarchy process Use of the appropriate maintenance strategy Management inefficiency [31] Gas Data envelopment analysis Sector policies Coal Oil [32] Gas authors and the key words suitable for the following search, done in the "Portal de Peri odicos Capes". To survey the research material the term "performance measurement system" was used. ...
Article
This article aims to identify the conditioning factors that influence the operation and maintenance performance of wind farms. The research method included theoretical research and case studies in seven companies that own wind farms. Theoretical research covered the following themes: performance management process; performance measurement in power plants, highlighting the factors that affect performance in this type of industry, and; the characteristics of the Brazilian wind energy market. The case studies were conducted through interviews with managers, whose positions are at the strategic, tactical and operational levels, with the objective of identify the performance management processes existing in wind farms. As a result, it was verified that the performance of wind farms is mainly influenced by 5 factors: (1) Reliability of prospecting studies, (2) Construction quality and assembly, (3) Organizational learning, and (5) Coordination of the value chain.
... Although we do not cover any particular applications in this chapter, we men- tion that the proposed approaches can be used for the coordination control purpose in wind farms (see [144]) under the time-varying nonlinear effects of wake which couple the down-stream turbines to the up-stream ones [164]. The large-scale power system with inter-area couplings can be viewed as another application for the proposed ideas of this chapter [165]. ...
Thesis
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Many large-scale systems can be modeled as groups of individual dynamics, e.g., multi-vehicle systems, as well as interconnected multiagent systems, power systems and biological networks as a few examples. Due to the high-dimension and complexity in configuration of these infrastructures, only a few internal variables of each agent might be measurable and the exact knowledge of the model might be unavailable for the control design purpose. The collective objectives may range from consensus to decoupling, stabilization, reference tracking, and global performance guarantees. Depending on the objectives, the designer may choose agent-level low-dimension or multiagent system-level high-dimension approaches to develop distributed algorithms. With an inappropriately designed algorithm, the effect of modeling uncertainty may propagate over the communication and coupling topologies and degrade the overall performance of the system. We address this problem by proposing single-and multi-layer structures. The former is used for both individual and interconnected multiagent systems. The latter, inspired by cyber-physical systems, is devoted to the interconnected multiagent systems. We focus on developing a single control-theoretic tool to be used for the relative information-based distributed control design purpose for any combinations of the aforementioned configuration, objective, and approach. This systematic framework guarantees robust stability and performance of the closed-loop multiagent systems. We validate these theoretical results through various simulation studies.
... According to the wake models, the active power loss of the downstream wind turbine is determined by the pitch angle and tip speed ratio of the upstream wind turbine. As a consequence, as presented in [10][11][12], compared with the MPPT method, the active power of the upstream wind turbine will be reduced by changing the pitch angle and tip speed ratio of the upstream wind turbine. However, the equivalent wind speed of the downstream wind turbine can be increased, which results in the active power increase of the downstream wind turbine. ...
... The goal of the DBO is to derive the optimum operating conditions or design parameters of the system to maximize or minimize a specific objective function. In energy engineering systems, the optimum operating conditions mostly result in minimizing the system total cost or maximizing the system energy production through system operating lifetime [10,11]. ...
Article
In the present study a comprehensive thermodynamic model and degradation based optimization framework for energy management of anode supported solid oxide fuel cell (SOFC) stacks are carried out. The optimization framework determines optimum operating conditions to maximize system productivity (energy generation over system lifetime) considering degradation mechanisms. The main degradation mechanisms in anode supported SOFCs are nickel coarsening and oxidation. In this study, the optimum operating conditions regarding these degradation mechanisms to achieve maximum productivity at different target lifetimes are derived. The results show that target lifetime has a significant impact on system productivity and optimum operating temperature and current density. Furthermore, SOFC optimum operating conditions as a function of target lifetime are derived. To show the effectiveness of the developed framework, model outputs are compared with two other operating strategies; a base case strategy that optimizes system operating conditions without considering degradation mechanisms and a strategy based on Department of Energy's (DOE) 2016 fuel cell report. Results illustrated that degradation based optimization is more beneficial for improving the entire performance in long-term operation. For instance, system productivity is 7.4% higher in comparison with DOE strategy during 40,000 h operating lifetime. It is expected that the proposed methodology will lead to more rapid commercialization of SOFC technology.
... According to the wake models, the active power loss of the downstream wind turbine is determined by the pitch angle and tip speed ratio of the upstream wind turbine. As a consequence, as presented in [10][11][12], compared with the MPPT method, the active power of the upstream wind turbine will be reduced by changing the pitch angle and tip speed ratio of the upstream wind turbine. However, the equivalent wind speed of the downstream wind turbine can be increased, which results in the active power increase of the downstream wind turbine. ...
... Operation and maintenance are conducted for optimized planning, routing and scheduling ( Dalgic et al., 2015a;Irawan et al., 2017;Rinaldi et al., 2017;Pillai et al., 2017). Moreover, the maximization of overall production is another way by optimizing the offshore wind farm layout (Chowdhury et al., 2012;González et al., 2015). carried out both onshore (Höfer et al., 2016;Noorollahi et al., 2016;Latinopoulos and Kechagia, 2015) and offshore wind farm planning ( Chaouachi et al., 2017;Fetanat and Khorasaninejad, 2015;Wu et al., 2016b). ...
... The elements studied extensively are: position of each turbine in the plant, wind direction, wind speed, turbulence and atmospheric stability and turbine type (generator torque, blade pitch angles or yaw angle). But consider the individual operating point of the wind turbines [10]. ...
... In conjunction with these wake models and wind farm data, control algorithms can be used to optimize yaw angles. Several studies have developed optimization-based control approaches to adjust to the uncertainties, using approaches such as Bayesian optimization [41], genetic algorithm [42], game theory [43,44], random search [45], sequential optimization [32], gradient descent [28,46], greedy control [47], particle swarm optimization [48], dynamic programming [49,50]. Most recently, Howland et al. [37] developed an control scheme based on an empirical fitted analytical wake model [26,40]. ...
Article
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One direction in optimizing wind farm production is reducing wake interactions from upstream turbines. This can be done by optimizing turbine layout as well as optimizing turbine yaw and pitch angles. In particular, wake steering by optimizing yaw angles of wind turbines in farms has received significant attention in recent years. One of the challenges in yaw optimization is developing fast optimization algorithms which can find good solutions in real-time. In this work, we developed a random search algorithm to optimize yaw angles. Optimization was performed on a layout of 39 turbines in a 2 km by 2 km domain. Algorithm specific parameters were tuned for highest solution quality and lowest computational cost. Testing showed that this algorithm can find near-optimal (<1% of best known solutions) solutions consistently over multiple runs, and that quality solutions can be found under 200 iterations. Empirical results show that as wind farm density increases, the potential for yaw optimization increases significantly, and that quality solutions are likely to be plentiful and not unique.
... The former concept de-rates upstream turbines to reduce the net thrust force exerted upon the wind, which in theory increases the average velocity within generated wakes that influence downstream machines (Johnson and Thomas, 2009). Evaluations of power de-rating have reported differing results, with larger efficiency gains corresponding to parametric wake models (Marden et al., 2013;Annoni et al., 2016;Ahmad et al., 2014;De-Prada-Gil et al., 2015;Mirzaei et al., 2015;Serrano González et al., 2015;Tian et al., 2014), and negligible gains or losses associated with computational fluid dynamics (CFD) https://doi.org/10.1016/j.oceaneng.2020.107445 Received 22 July 2019; Received in revised form 18 March 2020; Accepted 23 April 2020 simulations (Nilsson et al., 2015;Annoni et al., 2016;Dilip and Porté-Agel, 2017) and field tests (Boorsma, 2012;Schepers and Van Der Pijl, 2007). ...
Article
This work examines the steady-state potential and feasibility of Yaw and Induction-based Turbine Repositioning (YITuR), which is a wind farm control concept that passively repositions floating offshore wind turbines using existing turbine control degrees of freedom. To this end, the Floating Offshore Wind Farm Simulator (FOWFSim) is developed to model steady-state wind farm power production while considering floating platform relocation. Optimization studies are carried out with different floating wind farm design parameters and configurations. The objective is to determine sets of optimal wind turbine operating parameters that relocate floating turbines such that wind farm efficiency is maximized. Results show that the potential of YITuR is starkly limited by wind farm design parameters. In particular, anchors should be placed adequately far from floating platform neutral positions, mooring lines should be sufficiently long, and only specific mooring system orientations permit substantial gains in wind farm efficiency. With specific combinations of these parameters, simulation results show that the efficiency of a 7 × 7 floating offshore wind farm may be raised by 42.7% when implementing YITuR in comparison to greedy operation.
... Simulation studies of Horns Rev show that power output could be increased by 4.5%. In [76], GA is used to optimise the pitch angle and tip-speed ratio achieving an increase in AEP of 1.5%. In [77], a wind farm layout optimization is combined with control optimization. ...
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Abstract Wind farm control design is a recently new area of research that has rapidly become a key enabler for the development of large wind farm projects and their safe and efficient connection to the power grid. A comprehensive review of the intense research conducted in this area over the last 10 years is presented. Part I reviews control system concepts and structures and classifies them depending on their main objective (i.e. to maximise power production or to provide grid services. The work and key findings in each paper are discussed in detail with particular emphasis on the turbine side. Additionally, the review contributes to the existing reviews on the area by providing an elegant classification between model testing and control approaches. Areas where significant work is still needed are also discussed. In Part II, a thorough review on aerodynamic wind farm models for control design purposes is provided.
... An iterative gradient-based method considering the trust coefficient as the optimization variable to maximize wind farm output power is presented in Goit and Meyers. 18 In González ey al., 19 a genetic algorithm is used to optimize wind farm power by adjusting the blade pitch angle and the tip speed ratio of each wind turbine. In Zhong and Wang, 20 two model-free discrete adaptive filtering algorithms are proposed, and they are also extended for time-varying environmental condition uses. ...
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We study the application of cooperative control and game theoretic approaches to wind farm optimization. The conventional (greedy) wind farm control strategy seeks to individually maximize each turbine power. However, this strategy does not maximize the overall power production of wind farms due to the aerodynamic interactions (wake effect) between the turbines. We formulate the wind farm power optimization problem as an identical interest game which can also be used to solve other cooperative control problems. Two model‐free learning algorithms are developed to obtain the optimal axial induction factors of the turbines and maximize power production. The algorithms are simulated for a four‐turbine wind farm and the Princess Amalia wind farm and are compared to a learning algorithm that uses a game‐theoretic approach. It is shown that the proposed algorithms improve upon benchmark algorithm in terms of both performance and actuation effort.
... The goal of the DBO is to derive the optimum operating conditions or design parameters of the system to maximize or minimize a specific objective function. In energy engineering systems, the optimum operating conditions mostly result in minimizing the system total cost or maximizing the system energy production through system operating lifetime [10,11]. ...
Method
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In the present study a comprehensive thermodynamic model and degradation based optimization framework for energy management of anode supported solid oxide fuel cell (SOFC) stacks are carried out. The optimization framework determines optimum operating conditions to maximize system productivity (energy generation over system lifetime) considering degradation mechanisms. The main degradation mechanisms in anode supported SOFCs are nickel coarsening and oxidation. In this study, the optimum operating conditions regarding these degradation mechanisms to achieve maximum productivity at different target lifetimes are derived. The results show that target lifetime has a significant impact on system productivity and optimum operating temperature and current density. Furthermore, SOFC optimum operating conditions as a function of target lifetime are derived. To show the effectiveness of the developed framework, model outputs are compared with two other operating strategies; a base case strategy that optimizes system operating conditions without considering degradation mechanisms and a strategy based on Department of Energy's (DOE) 2016 fuel cell report. Results illustrated that degradation based optimization is more beneficial for improving the entire performance in long-term operation. For instance, system productivity is 7.4% higher in comparison with DOE strategy during 40,000 h operating lifetime. It is expected that the proposed methodology will lead to more rapid commercia-lization of SOFC technology.
... In that work, the authors considered a row of WTs, achieving an improvement of 4.5% in the aerodynamic power, compared to the conventional operating strategy. Serrano et al. [12] also presented a GA to maximise the total power of a WF by individually selecting the operating point of each of the turbines using the Park analytical model proposed by Katic and Jensen [13]. ...
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This paper presents a new approach based on the optimization of the blade pitching strategy of offshore wind turbines in order to maximize the global energy output considering the Gaussian wake model and including the effect of added turbulence. A genetic algorithm is proposed as an optimization tool in the process of finding the optimal setting of the wind turbines, which aims to determine the individual pitch of each turbine so that the overall losses due to the wake effect are minimised. The integration of the Gaussian model, including the added turbulence effect, for the evaluation of the wakes provides a step forward in the development of strategies for optimal operation of offshore wind farms, as it is one of the state-of-the-art analytical wake models that allow the evaluation of the energy output of the project in a more reliable way. The proposed methodology has been tested through the execution of a set of test cases that show the ability of the proposed tool to maximize the energy production of offshore wind farms, as well as highlights the importance of considering the effect of added turbulence in the evaluation of the wake.
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The impact of rotor setting and relative arrangement on the individual and overall power performance and aerodynamics of double rotor vertical axis wind turbine (VAWT) arrays is investigated. Eight rotor settings are considered: two relative rotational directions (co-rotating, CO, and counter-rotating, CN), two relative positionings (downstream turbine positioned in the leeward, LW, and windward, WW, of the upstream rotor), and two phase lags (Δθ = 0° and 180°). For each of the eight rotor settings, 63 different relative arrangements are considered resulting in 504 unique cases. The arrangements are considered within 1.25d ≤ R ≤ 10d (d = rotor diameter) and 0° ≤ Φ ≤ 90°, where R and Φ are relative distance and angle of the rotors, respectively. Unsteady Reynolds-Averaged Navier–Stokes (URANS) CFD simulations, validated with experimental data, are employed. The results show that the power performance of the array is significantly influenced by the relative rotational direction and positioning, ∼8% in power coefficient (CP), while it is marginally dependent on relative phase lag. The different performance of the studied arrays is because of different parts of the downstream turbine revolution being affected by the wake of the upstream turbine and dissimilar strength/width of the shear layer created in the two rotors’ wake overlap. The preferred rotational direction for WW arrays is co-rotating while for LW arrays counter-rotating is favored. For the same arrangement, counter-rotating turbines with LW relative positioning have the highest CP due to their downstream turbine blade moving along the flow direction in the wake overlap region resulting in little energy dissipation and weak shear layer. In contrast, counter-rotating arrays with WW relative positioning have the lowest CP, because the downstream turbine blade moves against the flow in the wake overlap region, resulting in extensive velocity deficit and a thick, strong shear layer.
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Wake effects in a wind farm (WF) include the wind velocity deficit and added turbulence. The wind velocity deficit may bring significant loss of the wind power and the added turbulence may cause extra fatigue load on the wind turbines (WTs). Inclusion of the wake effects in the wind farm control design can increase the total captured power by derating the upwind WTs. However, this may increase the turbulence and cause more fatigue load on the downwind WTs. This paper proposes an optimized active power dispatch strategy for WFs to maximize the total captured power while maintaining the fatigue load of the shafts and the towers within a certain range from the values using traditional strategy, which adopts maximum power point tracking (MPPT) control for each WT. A WT derating control strategy is included in the WT controller and the fatigue load for the tower and shaft is evaluated offline at a series of turbulence intensity, mean wind speed and active power reference to form a lookup table, which is used for the WF control. The proposed strategy is compared with WT MPPT control strategy and WF MPPT control strategy. The simulation results show the effectiveness of the proposed strategy.
Article
Traditional methods for micro-siting of wind farms usually assume that each turbine is controlled to obtain its individual maximum power generation. However, it is now common practice to implement farm level control actions during the daily operations in order to improve the economic profit of an entire wind plant. This study is then proposed to investigate the wind farm layout optimization to achieve the minimum cost per unit of energy (CoE), when the farm level control operations, the coordination of turbine cooperations (CTC), is considered preliminary farm planning stage. It is found that, by optimizing the turbine number and layout with CTC involved, the efficiency of a wind farm can be further improved. A single-objective optimization problem is firstly established by modelling all aforementioned factors of interest. Subsequently, a hybrid optimization algorithm is proposed, with a greedy algorithm to optimize the turbine number and a particle swarm optimization (PSO) algorithm to refine the turbine layout scheme. Eventually, extensive simulation studies with various scenarios are provided to substantiate the feasibility of the proposed scheme.
Chapter
The investigation of wind farms attracts lots of researchers globally although their investment cost is quite high. Wind power, as a relatively new technology, still keeps enough research space on the study of cost reduction methods. Even a small improvement in the wind farm control or wind farm optimization can lead to a large sum of money saving. Hence, many works aim to maximize the energy production or minimize the investment cost of the whole wind farms. This chapter is organized as follows: Section 21.2 introduces wind farm active power dispatch methods. Wind farm reactive power dispatch is analyzed in Section 21.3. Wind farm layout optimization problem is presented in Section 21.4. In the end, this chapter is summarized in Section 21.5.
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Due to the uncertainty in output power of wind farm (WF) systems, a certain reserve capacity is often required in the power system to ensure service reliability and thereby increasing the operation and investment costs for the entire system. In order to reduce this uncertainty and reserve capacity, this study proposes a multi-objective stochastic optimization model to determine the set-points of the WF system. The first objective is to maximize the set-point of the WF system, while the second objective is to maximize the probability of fulfilling that set-point in the real-time operation. An increase in the probability of satisfying the set-point can reduce the uncertainty in the output power of the WF system. However, if the required probability increases, the set-point of the WF system decreases, which reduces the profitability of the WF system. Using the proposed method helps the WF operator in determining the optimal set-point for the WF system by making a trade-off between maximizing the set-point of WF and increasing the probability of fulfilling this set-point in real-time operation. This ensures that the WF system can offer an optimal set-point with a high probability of satisfying this set-point to the power system and thereby avoids a high penalty for mismatch power. In order to show the effectiveness of the proposed method, several case studies are carried out, and the effects of various parameters on the optimal set-point for the WF system are also analyzed. According to the parameters from the transmission system operator (TSO) and wind speed profile, the WF operator can easily determine the optimal set-point using the proposed strategy. A comparison of the profits that the WF system achieved with and without the proposed method is analyzed in detail, and the set-point of the WF system in different seasons is also presented.
Preprint
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A practical wind farm controller for production maximisation based on coordinated control is presented. The farm controller emphasises computational efficiency without compromising accuracy. The controller combines Particle Swarm Optimisation (PSO) with a turbulence intensity based Jensen wake model (TI-JM) for exploiting the benefits of either curtailing upstream turbines using coefficient of power ($C_P$) or deflecting wakes by applying yaw-offsets for maximising net farm production. First, TI-JM is evaluated using convention control benchmarking WindPRO and real time SCADA data from three operating wind farms. Then the optimized strategies are evaluated using simulations based on TI-JM and PSO. The innovative control strategies can optimise a medium size wind farm, Lillgrund consisting of 48 wind turbines, requiring less than 50 seconds for a single simulation, increasing farm efficiency up to a maximum of 6% in full wake conditions.
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Towards connectivity and development of reliable systems, smarting sensors are vastly applied in hi-tech industries. The wind energy is a growing market that could benefit from edge processing technology by enhancing monitoring systems, decreasing downtime and guiding predictive maintenance. We proposed an embedded multi-sensor architecture to detect incipient short-circuit in wind turbine electrical generators, that is robust to both false positives and negatives. Five different sensor settings are tested in three feature extraction methods and four classifiers. An analysis of variance (ANOVA) and a Tukey’s honestly significant difference (HSD) statistical tests are used to determine which architectures should be embedded in a Raspberry Pi 3, NVIDIA Jetson TX2 and NVIDIA Xavier boards. A three current sensor setting with Fourier-MLP is the most suitable approach, achieving 81.20% of accuracy, 0% of false positive rate (FPR) and 0.08% of false negative rate (FNR), also detecting generator’s normal conditions 100% of the time. For a single sensor configuration, current sensor is the most suitable method for detecting fault or non-fault conditions, being 16 times more robust to false negatives than using an axial flux sensor. Comparing the processing time, the system embedded in a NVIDIA Xavier predicts a fault condition 37% faster than in a Raspberry Pi 3, with Fourier-MLP and using a single current sensor, thus being the most suitable configuration in fault detection.
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As the emergence of large-scale offshore wind farms (WFs), how to reduce the high cost of them has become a critical problem. One way is to decrease the maintenance cost, another way is to capture more wind energy. Herein, an optimised WF active power dispatch (APD) strategy is proposed to make WF capture the maximum wind energy while balancing the fatigue distribution of WF, which is closely related to the WF maintenance frequency. In addition, two traditional strategies are introduced as comparisons of the proposed strategy. And the result of strategy A is used as a benchmark for comparison. This paper takes fatigue coefficient to evaluate the fatigue load suffered by wind turbines (WT). Particle swarm optimization (PSO) is adopted to solve this problem. Simulations are conducted based on a regular shaped WF with 25 WTs and an irregular shaped WF with 80 WTs. The results of case studies prove the superiority of the strategy formulated herein.
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Wind energy is an interesting source of alternative energy to complement the Brazilian energy matrix. However, one of the great challenges lies in managing this resource, due to its uncertainty behavior. This study addresses the estimation of the electric power generation of a wind turbine, so that this energy can be used efficiently and sustainable. Real wind and power data generated in set of wind turbines installed in a wind farm in Ceará State, Brazil, were used to obtain the power curve from a wind turbine using logistic regression, integrated with Nonlinear Autoregressive neural networks to forecast wind speeds. In our system the average error in power generation estimate is of 29 W for 5 days ahead forecast. We decreased the error in the manufacturer's power curve in 63%, with a logics regression approach, providing a 2.7 times more accurate estimate. The results have a large potential impact for the wind farm managers since it could drive not only the operation and maintenance but management level of energy sells.
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Wind energy is becoming the fastest growing and most inexpensive renewable source, even surpassed natural gas. The environmental advantages coupled with the significant financial benefits have created a positive prognosis for wind energy continuously growing. However, the complexity and limited availability of wind resources create challenges that need to be addressed in order to continue improving wind energy harvesting. This paper developed a new concept to modify wind farm’s layout by deactivating selected wind turbines to maximize its total power output under different wind conditions. Different wind conditions create different wake effects, while most wind farms cannot change their layouts to cope with the changing wind conditions. Through deactivating selected wind turbines to effectively reduce or eliminate some turbulent wakes, it is possible to improve a wind farm’s total power output by creating a net gain for the entire wind farm. A new method was developed to identify the best combinations of deactivated wind turbines under different wind conditions to achieve maximum power output. Several case studies with real wind farms and real wind conditions were conducted together with sensitivity analysis. The promising results demonstrated the effectiveness of the new method and the new concept, named layout optimization through selective deactivation. Several factors were identified as influencing factors on the effectiveness of the new concept. 50 days' free access link https://authors.elsevier.com/c/1YmJmin8VW9BN
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We develop a mixed graph and optimal control theoretic formulation to design a robust cooperative control protocol for a large‐scale multiagent system with partially known interconnected first‐, second‐, or mixed first‐ and second‐order dynamics. In each case, we transform the control protocol design task to a robust communication graph design problem, which, from a cyber‐physical perspective, is interpreted as the control layer design problem for an interconnected system with unknown agent layer dynamics. According to this viewpoint, each state variable has its own control layer communication topology separate from the other state variable's communication topology and the unknown agent layer interconnection topologies. We prove that all cooperative, decentralized, and centralized tracking protocols can be treated as a single design problem and, by deriving closed‐form solutions for the robust control layer topologies, we further provide a simpler design procedure, which is only based on the matrix manipulations. Aside from the linear implementation of the protocol and the connection of the proposed formulation to the well known rules‐of‐thumb in optimal control theory, this creates a higher potential to transfer ideas to industry. Modeling uncertainties tolerable by a given control layer topology is analyzed, and a preliminary performance‐oriented analysis and design approach for large‐scale interconnected systems is discussed. We show that exactly the same steps can be followed to design appropriate control layers for both tracking and stabilization.
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The power dispatch strategy for onshore wind farms generally focuses on maximizing the captured power or minimizing the investment cost. However, as for onshore wind farms, there are some environmental impacts that need to be considered if needed. Among them, the wind turbine (WT) noise is a fairly obvious environmental impact. Noise caused by wind farms may cause interference to the surrounding living environment and the power dispatch strategy should combine power production and environmental factors. Besides, the amount of electricity generated by onshore wind farms is also affected by topography and power losses. In this paper, an optimal power dispatch strategy of wind farms with limited WTs noise impact is proposed for a better environmental performance as well as maximizing the power generation. The new method is compared with the traditional MPPT method for single WT and the improved global MPPT method for the whole wind farm within two terrain scenarios. The case results show the feasibility and effectiveness of this novel strategy.
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The wake regulation by cooperative yaw control, axis induction control, or their combination can significantly improve the total power output of wind farms. However, a comprehensive study of the three control strategies is still lacking, which hinders their engineering applications. To this end, the optimized yaw angles, induction factors, and total power promotion are systematically compared for the three control strategies on both a regularly arranged wind farm and an irregularly arranged realistic offshore wind farm. The results indicate that all the three strategies work similarly to achieve a net power gain by enhancing the power output of the downstream turbines while slightly downgrading the upstream turbines. For the regularly arranged wind farm, very notable power promotions are obtained in the aligned direction, and all the three control strategies show similar performance, but the promotions almost disappear with a small misalignment. In contrast, power promotion is less sensitive to the wind direction for the irregularly arranged wind farm. The promotion of the yaw control is much more predominate than that of the induction control, and the combined control only slightly outperforms the single yaw control in the realistic wind farm. In the prevailing wind direction, the total power of the realistic wind farm can be increased by 2.1% by the combined control strategy under the wind speed of 6 m/s.
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This article presents a review of control strategies for maximizing power production within wind farms. Discussions focus on three notable concepts; power de-rating, yaw-based wake redirection, and turbine repositioning. Existing works that have examined the potential of these concepts via optimization studies, numerical simulation, experimentation, as well as those that have developed and evaluated control algorithms, are reviewed thoroughly and quantitatively. Criteria for this review process include the evaluation methods employed, simulated wind conditions, controller properties such as model dependency and communication architecture, and the resulting relative rise in wind farm efficiency. The data collected from existing literature is then utilized to draw conclusions regarding the influence of each of these criteria on the potential and performance of wind farm controllers. Appropriate recommendations for future modeling and controller design research are then offered based on these conclusions.
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In later years, research in mobile robotic areas have been experiencing a growth in interest due to its vast application area. In an unknown environment, the robot's location and movement are essential for its operation. In addition, machine learning techniques, along with signal or image processing, have been applied to map the environment, locate and move the mobile robot. This article proposes a low cost and efficient approach for mobile robot localization. It uses a omnidirectional sonar with machine learning and image processing. The feature extractors used in this paper were: Structural Co-occurrence Matrix (SCM), Statistical Moments, Central Moments, Hu Moments and Gray Level Co-occurrence Matrix (GLCM). The classifiers used in this study were: Bayes classifier, k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Optimum Path Forest (OPF) and Support Vector Machines (SVM). The results showed that the best accuracy was achieved with Central Moments as feature extractor and OPF as classifier, achieving 96.61% and with a test time of 100us.
Conference Paper
Wind energy is gaining importance as one the most progressive renewable energies due to rapid depletion of conventional energy resources. Micro-siting is the optimal way of placing turbines inside a wind farm to convert wind power into electrical energy avoiding constraints related to wake loss. Though a significant progress has been made towards proposing efficient methodologies for micro-siting, limited availability of land area has resulted in the construction of wind farms near to the human habitats causing a negative impact on the human health. Compared to the other effects, the effect of noise is a matter of immense concern for the wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on multi-objective trade-off between minimization of the noise propagation and maximization of the energy generation. A novel hybrid methodology is proposed as a combination of probabilistic multi-objective evolutionary algorithm (NSGA-II) and a deterministic gradient based Normalized normal constraint (NNC) method. In contrast to previous studies, the generated Pareto Optimal (PO) front provides several options for a decision maker, where optimal number of turbines and their optimal layouts are obtained at the same time along with the alternative solutions.
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In the present work our focus is to improve the performance of a wind farm by coordinated control of all turbines with the aim to increase the overall energy extraction by the farm. To this end, we couple flow simulations performed using Large Eddy Simulations (LES) with gradient based optimization to control individual turbines in a farm. The control parameters are the disk-based thrust coefficient of individual turbines as a function of time. They indirectly represent the effect of control actions that would correspond to blade-pitching of the turbines. We employ a receding-horizon predictive control setting and solve the optimization problem iteratively at each time horizon based on the gradient information obtained from the evolution of the flow field and the adjoint computation. We find that the extracted farm power increases by approximately 16% for a cost functional that is based on total energy extraction. However, this energy is gained from a slow deceleration of the boundary layer which is sustained for approximately 1 hour. We further analyze the turbulent stresses and compare to wind farms without optimal control.
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The present report is the publishable final activity report for the EU project TOPFARM. The project has been running from 1 st December 2007 to 30 th November 2010, and has successfully addressed optimization of wind farm topology and control strategy based on aero-elastic modeling of loads as well as of power production as seen in an economical perspective. Crucial factors in this regard are the overall wind climate at the wind farm site, the position of the individual wind turbines, the wind turbine characteristics, the internal wind farm wind climate, the wind turbine control/operation strategy for wind turbines interacting through wakes, various cost models, the optimization strategy and a priori defined constraints imposed on the wind farm topology. In TOPFARM, the object function used in the optimization platform is formulated in economical terms, thus ensuring the optimal balance between capital costs, operation and maintenance costs, cost of fatigue lifetime consumption and power production output throughout the design lifetime of the wind farm. The report describes the project consortium and the project activities, which has been organized in 9 Work Packages. A summary description of the results is given, and reference is made to a large number of publications resulting from the project.
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The objective of this study is to improve the cost-effectiveness and production efficiency of wind farms using cooperative control. The key factors in determining the power production and the loading for a wind turbine are the nacelle yaw and blade pitch angles. However, the nacelle and blade angles may adjust the wake direction and intensity in a way that may adversely affect the performance of other wind turbines in the wind farm. Conventional wind-turbine control methods maximize the power production of a single turbine, but can lower the overall wind-farm power efficiency due to wake interference. This paper introduces a cooperative game concept to derive the power production of individual wind turbine so that the total wind-farm power efficiency is optimized. Based on a wake interaction model relating the yaw offset angles and the induction factors of wind turbines to the wind speeds experienced by the wind turbines, an optimization problem is formulated with the objective of maximizing the sum of the power production of a wind farm. A steepest descent algorithm is applied to find the optimal combination of yaw offset angles and the induction factors that increases the total wind farm power production. Numerical simulations show that the cooperative control strategy can increase the power productions in a wind farm.
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This paper gives an evaluation of most of the commonly used models for predicting wind speed decrease (wake) downstream of a wind turbine. The evaluation is based on six experiments where free-stream and wake wind speed profiles were measured using a ship-mounted sodar at a small offshore wind farm. The experiments were conducted at varying distances between 1.7 and 7.4 rotor diameters downstream of the wind turbine. Evaluation of the models compares the predicted and observed velocity deficits at hub height. A new method of evaluation based on determining the cumulative momentum deficit over the profiles is described. Despite the apparent simplicity of the experiments, the models give a wide range of predictions. Overall, it is not possible to establish any of the models as having individually superior performance with respect to the measurements.
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The proposed model for the wind speed deficit in wind farms is analytical and encompasses both small wind farms and wind farms extending over large areas. As is often the need for offshore wind farms, the model handles a regular array geometry with straight rows of wind turbines and equidistant spacing between units in each row and equidistant spacing between rows. Firstly, the case with the flow direction being parallel to rows in a rectangular geometry is considered by defining three flow regimes. Secondly, when the flow is not in line with the main rows, solutions are suggested for the patterns of wind turbine units corresponding to each wind direction. The presentation is an outline of a model complex that will be adjusted and calibrated with measurements in the near future. Copyright © 2006 John Wiley & Sons, Ltd.
Conference Paper
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This paper proposes a novel approach to optimal placement of wind turbines in the continuous space of a wind farm. The control objective is to maximize the power produced by a farm with a fixed number of turbines while guaranteeing the distance between turbines no less than the allowed minimal distance for turbine operation safety. The problem of wind farm micro-siting with space constraints is formulated to a constrained optimization problem and solved by a particle swarm optimization (PSO) algorithm based on penalty functions. Simulation results demonstrate that the PSO approach is more suitable and effective for micro-siting than the classical binary-coded genetic algorithms.
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Turbines operating in wind farms are coupled by the wind flow. This coupling results in limited power production and increased fatigue loads on turbines operating in the wake of other turbines. To operate wind farms cost effectively, it is important to understand and address these effects. In this paper, we derive a stationary model for turbine interaction. The model has a simple intuitive structure, and the parameters have a clear interpretation. Moreover, the effect of upwind turbines on a downwind turbine can be completely determined through information from its closest neighbor. This makes the model well-suited for distributed control. In an example, we increase total power production in a farm, by coordinating the individual power production of the turbines. The example points to an interesting model property: decreasing power in an upwind turbine causes downwind turbines to pose less of an obstacle for the wind, provided that they maintain their level of power capture.
Conference Paper
By extracting kinetic energy from the wind flow, a wind turbine reduces the wind speed in the wake downstream of the wind turbine rotor. In a wind power plant, this wake effect reduces the power production of downstream turbines. This paper presents a control scheme for optimizing the total power output of a wind power plant by taking into account the wake effect. It is a distributed control scheme in which each wind turbine adapts its control settings based on information that it receives from neighbouring turbines. The total power optimization is performed using gradient-based optimization. The optimization is done in a model-free, data-driven manner, as the gradients are estimated from the past control actions, the measured power response of the turbine itself, and the power response of neighbouring turbines. The time-efficiency of the optimization scheme was improved by exploiting information on the locations of the turbines in the wind plant, and an estimate of the wind direction. The method is tested in a simulation of the Princess Amalia Wind Park. To be able to evaluate the time-efficiency of the scheme, in the simulation model a delay structure was included that models the wake traveling from one turbine to the next. The new control method results in a much faster convergence of the power optimization when compared with an existing model-free wind plant power optimization method that uses a game theoretic approach.
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This brief explores the applicability of recent results in game theory and cooperative control to the problem of optimizing energy production in wind farms. One such result is a model-free control strategy that is completely decentralized and leads to efficient system behavior in virtually any distributed system. We demonstrate that this learning rule can provably maximize energy production in wind farms without explicitly modeling the aerodynamic interaction amongst the turbines.
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In this paper a novel evolutionary algorithm for optimal positioning of wind turbines in wind farms is proposed. A realistic model for the wind farm is considered in the optimization process, which includes orography, shape of the wind farm, simulation of the wind speed and direction, and costs of installation, connection and road construction among wind turbines. Regarding the solution of the problem, this paper introduces a greedy heuristic algorithm which is able to obtain a reasonable initial solution for the problem. This heuristic is then used to seed the initial population of the evolutionary algorithm, improving its performance. It is shown that the proposed seeded evolutionary approach is able to obtain very good solutions to this problem, which maximize the economical benefit which can be obtained from the wind farm.
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An Evolutive Algorithm (EA) for wind farm optimal overall design is presented. The algorithm objective is to optimize the profits given an investment on a wind farm. Net Present Value (NPV) will be used as a figure of the revenue in the proposed method. To estimate the NPV is necessary to calculate the initial capital investment and net cash flow throughout the wind farm life cycle. The maximization of the NPV means the minimization of the investment and the maximization of the net cash flows (to maximise the generation of energy and minimise the power losses). Both terms depend mainly on the number and type of wind turbines, the tower height and geographical position, electrical layout, among others. Besides, other auxiliary costs must be to keep in mind to calculate the initial investment such as the cost of auxiliary roads or tower foundations. The difficulty of the problem is mainly due to the fact that there is neither analytic function to model the wind farm costs nor analytic function to model net generation. The complexity of this problem arises not only from a technical point of view, due to strong links between its variables, but also from a purely mathematical point of view. The problem consists of both discrete and continuous variables, being therefore an integer-mixed type problem. The problem exhibits manifold optimal solutions (convexity), some variables have a range of non allowed values (solutions space not simply connected) and others are integers. This fact makes the problem non-derivable, preventing the use of classical analytical optimization techniques.
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The control system design for a wind power plant is investigated. Both the overall wind farm control and the individual wind turbine control effect the wind farm dynamic performance.For a wind turbine with a synchronous generator and rectifier/inverter system a multivariable controller is designed. Using the optimal output feedback method a compromise is found between speed, power fluctuations and mechanical load. Nonlinear simulations show the superior performance compared to a classical control design.Preliminary results for the overall wind farm control show that the compensation of aerodynamic interactions between the wind turbines for energy maximisation is beneficial. Load control is even more important, especially in combination with wind prediction models.
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This article provides an overview and analysis of different wake-modelling methods which may be used as prediction and design tools for both wind turbines and wind farms. We also survey the available data concerning the measurement of wind magnitudes in both single wakes and wind farms, and of loading effects on wind turbines under single- and multiple-wake conditions. The relative merits of existing wake and wind farm models and their ability to reproduce experimental results are discussed. Conclusions are provided concerning the usefulness of the different modelling approaches examined, and difficult issues which have not yet been satisfactorily treated and which require further research are discussed. Copyright
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This report describes a three-bladed, upwind, variable-speed, variable blade-pitch-to-feather-controlled multimegawatt wind turbine model developed by NREL to support concept studies aimed at assessing offshore wind technology.
Article
A new methodology, the Unrestricted Wind Farm Layout Optimization (UWFLO), that addresses critical aspects of optimal wind farm planning is presented in this paper. This methodology simultaneously determines the optimum farm layout and the appropriate selection of turbines (in terms of their rotor diameters) that maximizes the net power generation. The farm layout model obviates traditional restrictions imposed on the location of turbines. A standard analytical wake model has been used to account for the velocity deficits in the wakes created by individual turbines. The wind farm power generation model is validated against data from a wind tunnel experiment on a scaled down wind farm. Reasonable agreement between the model and experimental results is obtained. The complex nonlinear optimization problem presented by the wind farm model is effectively solved using constrained Particle Swarm Optimization (PSO). It is found that an optimal combination of wind turbines with differing rotor diameters can appreciably improve the farm efficiency. A preliminary wind farm cost analysis is performed to express the cost in terms of the turbine rotor diameters and the number of turbines in the farm. Subsequent exploration of the influences of (i) the number of turbines, and (ii) the farm land size, on the cost per Kilowatt of power produced, yields important observations.Highlights► An array layout or a grid-based layout pattern is not assumed. ► A variable induction factor and a partial wake-rotor overlap are accounted for. ► Using turbines with differing rotor diameters increased the farm power remarkably. ► Selecting an appropriate land area per turbine is crucial to optimal farm design. ► The PSO algorithm is suitable for wind farm layout optimization.
Article
We consider a farm as a single energy extracting body instead of a superposition of individual energy extractors i.e. wind turbines. As a result we found two new hypotheses called Heat and Flux. Both hypotheses reveal that the classical operation of turbines in a wind farm at the Lanchester-Betz optimum does not lead to maximum farm output. However, when the turbines at the windward side of the farm are operated below their optimum, then the power of the turbines under the lee increases in such a way that the net farm production increases slightly. Next to this production advantage of Heat and Flux operation there is also a loading advantage. The average axial loading of the upwind turbines of a farm is reduced in a 'Heat and Flux'-farm. As a result those turbines generate less turbines so that the fatigue loads of the downwind turbines reduce too. The results were confirmed by in a boundary layer tunnel by means of differential measurements between a 'Heat and Flux'-farm and a classical farm.
Article
A genetic algorithm approach is employed to obtain optimal placement of wind turbines for maximum production capacity while limiting the number of turbines installed and the acreage of land occupied by each wind farm. Specifically, three cases are considered—(a) unidirectional uniform wind, (b) uniform wind with variable direction, and (c) non-uniform wind with variable direction. In Case (a), 600 individuals are initially distributed over 20 subpopulations and evolve over 3000 generations. Case (b) has 600 individuals spread over 20 subpopulations initially and evolves for 3000 generations. Case (c) starts with 600 individuals spread over 20 subpopulations and evolves for 2500 generations. In addition to optimal configurations, results include fitness, total power output, efficiency of power output and number of turbines for each configuration. Disagreement with the results of an earlier study is observed and a possible explanation is provided.
Article
Modeling of turbulence within wind farms with 100 or more wind turbines is important both for extreme and fatigue limit states. Behind a wind turbine a wake is formed where the mean wind speed decreases slightly and the turbulence intensity increases significantly. This increase in turbulence intensity in wakes behind wind turbines can imply a significant reduction in the fatigue lifetime of wind turbines placed in wakes. In this paper the design code model in the wind turbine code [IEC 61400-1, Wind turbine generator systems — Part 1: Safety requirements. 2005] is evaluated from a probabilistic point of view, including the importance of modeling the SN-curve by a bi-linear model. Fatigue models relevant for welded, cast steel and fiber reinforced details are considered. Further, the influence on the fatigue reliability is investigated from modeling the fatigue response by a stochastic part related to the ambient turbulence and the eigenfrequencies of the structure and a deterministic, sinusoidal part with frequency of revolution of the rotor.
Article
David Goldberg's Genetic Algorithms in Search, Optimization and Machine Learning is by far the bestselling introduction to genetic algorithms. Goldberg is one of the preeminent researchers in the field--he has published over 100 research articles on genetic algorithms and is a student of John Holland, the father of genetic algorithms--and his deep understanding of the material shines through. The book contains a complete listing of a simple genetic algorithm in Pascal, which C programmers can easily understand. The book covers all of the important topics in the field, including crossover, mutation, classifier systems, and fitness scaling, giving a novice with a computer science background enough information to implement a genetic algorithm and describe genetic algorithms to a friend.
The economics of wind energy. European Wind Energy Association
  • S Krohn
  • P E Morthorst
  • S Awerbuch
Krohn S, Morthorst PE, Awerbuch S. The economics of wind energy. European Wind Energy Association; 2009. p. 28e9.
More power and less loads in wind farms:'heat and flux'. In: European wind energy conference & exhibition
  • Corten G P Schaak
  • Bot
Corten G, Schaak P, Bot E. More power and less loads in wind farms:'heat and flux'. In: European wind energy conference & exhibition, London, UK; 2004.
Optimal control of wind power plants
  • M Steinbuch
  • W De Boer
  • O Bosgra
  • S Peters
  • J Ploeg
Steinbuch M, de Boer W, Bosgra O, Peters S, Ploeg J. Optimal control of wind power plants. J Wind Eng Ind Aerodyn 1988;27:237e46.