Advanced Control of Solar Plants
Abstract
Preface.- List of figures.- List of tables.- Nomenclature.- Introduction.- Description and dynamic models of the plant.- Basic control schema.- Basic structures of adaptive control.- Model-based predictive control strategies.- Frequency-domain control and robust optimal control.- Heuristic fuzzy logic control.- Summary and concluding remarks.- References.- Index.
Chapters (7)
The objective of this chapter is to describe the distributed collector field and the models used for its simulation and control. The process considered is the distributed solar collector field ACUREX of the “Plataforma Solar de Almería” (PSA). This is an installation which belongs to the Spanish “Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas” (CIEMAT) of the Ministry of Industry, Commerce and Tourism, and is jointly operated by the CIEMAT and the “Deutsche Forschungsanstal für Luft-und Raumfahrt” (DLR), of Germany. It is the largest European test center for solar thermal energy applications.
This chapter deals with the development of feedforward controllers, whose introduction within the control loop plays an important role because of their capabilities to reject measurable disturbances which act on the plant, such as solar radiation and inlet oil temperature changes. The chapter also comprises the description of some basic fixed PID control strategies in order to analyze the behaviour obtained with these classical control techniques.
This chapter deals with the description of the parameter estimation algorithm used in all the developed adaptive control schema, including the supervisory mechanisms needed for assuring some degree of robustness and performance. The chapter also includes the description of some adaptive PID control strategies.
In this chapter, four predictive control strategies are introduced. The first control strategy presented uses a reduced order linear model of the plant to obtain the control law, which parameters are changed on-line within an indirect adaptive control strategy. The parameters of the simplified linear model are identified and used to properly modify the controller parameters. Problems associated with the convergence of the identifier and errors due to unmodelled dynamics are studied. These modelling errors can be critical in the kind of systems studied here, due to the presence of antiresonance modes, which can be situated within the bandwidth of the controlled system if a fast response or good disturbance rejection properties are required to the controlled system.
In this chapter, two control schema based on the frequency response of the open loop system are studied.
Since Mamdani [79] published his experiences using a fuzzy logic controller on a test-bed plant of a laboratory, many different control approaches have appeared based on this theory and also many applications of this type of controller to a diversity of processes such as warm water plants [60], rotative clinker-cooker furnaces for cement production [69], [61], automatic train operation [123], cargo ship steering [71], robotics [108], [107], and others. An extensive introduction to the historical development, state and concepts involving fuzzy control systems can be found in [72], [73].
The aim of this chapter is to provide a brief summary of the main features of the different control schema included in the book. The most significant characteristics of control structures have been described in the chapters devoted to commenting on the development and inmplementation of the several controllers. Here, these characteristics are summarized and the performance and robustness properties of controllers are compared. In order to carry out this comparison, the nonlinear distributed parameters simulation model has been used, as a way of obtaining more realistic results than those which would be obtained performing the study using different simplified linear models. The objective of the different advanced control schema when applied to the distributed collector field, is to provide acceptable behaviour throughout the entire range of operating conditions in spite of disturbances acting on the system. The simulation used here for performance and robustness analysis purposes covers different operating conditions and disturbances.
... The heat transfer in a concentrated distributed solar collector is described by a first order hyperbolic PDE. It is obtained by neglecting the thermal exchange and the heat losses between the thermal carrier and the collector tube, as illustrated in Based on an energy balance analysis, the dynamics of the process is given by [7]: ...
... , J and J is the number of modulating functions, q ∈ R + is a degree-of-freedom parameter which tunes the order of modulating functions. Through the entire simulation, the parameter q is fixed to 3. The model's parameters of the concentrated distributed solar collector are chosen to be ρ = 903, c = 1820, A s = 0.0006, v = 0.73, G = 1.83, and L = 172 according to [7]. The proposed method has been tested by considering two scenarios, which are synthetic and real solar irrandiance. ...
... COLLECTOR PARAMETERS[7] ...
... Easy Java Simulations (EJS) is one of the most common programming environment to develop virtual labs (Esquembre, 2008(Esquembre, , 2015, and it was selected to create the tool presented in this paper. The resulting virtual lab allows analyzing the nonlinear behaviour of the solar collector field model described by (Álvarez et al., 2009;Camacho et al., 2012). A solar radiation model is included that simulates a clear day for a selected date on the graphical interface. ...
... (1), can be approximated to the term (T out − T in /L) (Camacho et al., 2012). Later, if the pipe temperature, T w , is found from Eq. ...
... For this reason, a second control mode is proposed, where parallel feedforward configuration (Camacho et al., 2012) is included in the control scheme, see Fig. 4. The feedforward is implemented as a static version of the solar field model presented in Eq. (4), which is in charge of providing the nominal operating point in terms of water flow rate (F F F sf (t)), according to the operating conditions and disturbances (T a (t), I(t), and T in (t)), and the outlet temperature setpoint (T SP out (t)). In this way, the control signal in this control mode (see Fig. 4) is calculated as: ...
Research and teaching on renewable energies are very important topics worldwide. Most of the renewable energy processes are quite complex to analyze and to study. For that reason, educational resources as support for the teaching and learning of these processes dynamics are very useful. Thus, this paper presents a virtual lab as support to the modeling and control concepts of a solar collector field, as these kind of solar plants are widely used at industrial level requiring control skills. This interactive tool is used as part of several subjects in a Master on Solar Energy at the University of Almería, Spain. Examples about open and closed-loop exercises are presented.
... Un esquema de la planta se muestra en la Fig. 1. Para obtener una descripción completa de la planta y el procedimiento de modelado, consulte (Carmona, 1985;Camacho et al., 1997). ...
... Las dinámicas de un campo solar distribuido se pueden modelar mediante el siguiente sistema de ecuaciones en derivadas parciales que describen un balance energético (Carmona, 1985;Camacho et al., 1997): ...
... La densidad ρ, el calor específico C y el coeficiente de transmisión de calor H t del fluido caloportador dependen de la temperatura del fluido. El coeficiente de transmisión de calor depende de la temperatura y el flujo de aceite (Camacho et al., 1997). ...
p>La estimación de estados no observables de un proceso es importante cuando se aplican técnicas de control automático basadas en el espacio de estados. El diseño y uso de estos controladores conlleva suponer que los valores de los estados se podrán obtener de una u otra forma. Cuando todos los estados son medibles, no existe la necesidad de aplicar observadores. El caso de plantas solares cilindro-parabólicas usando modelos de parámetros distribuidos presenta el problema de no poder medir todos los valores de los estados con sensores. En este trabajo se presenta un observador basado en un sistema de inferencia borrosa para estimar los perfiles de temperatura de los lazos que componen el campo solar. Se aplica una técnica de reducción de complejidad basada en el análisis de componentes principales funcionales para que el estimador sea realizable en la práctica sin ocupar mucha memoria o consumir demasiado tiempo tanto en la computacion del algoritmo como en la programación en dispositivos industriales.</p
... En este trabajo se usa el modelo de parámetros distribuidos para realizar simulaciones y evaluar el desempeño del controlador propuesto. El modelo de parámetros distribuidos se puede describir con dos ecuaciones diferenciales en derivadas parciales [15] del siguiente modo: ...
... Si el modelo es no lineal, el problema de optimización requiere la resolución de un problema de programación no lineal, el cual es computacionalmente más costoso de resolver y alcanzar elóptimo global no está asegurado. Particularmente en un proceso como el sistema de energía solar de este artículo, para poder resolver el problema no lineal en el tiempo de muestreo requerido, es necesario elegir horizontes pequeños [15]. ...
... Donde y ref es la referencia de temperatura, S es la superficie reflectante de 352 m 2 y P cp es un término que incluye la entalpía del fluido y otros factores geométricos [15]. La figura 3, muestra el esquema de control final: ...
... A distributed parameter model is used to simulate the 50 MW solar trough field while a concentrated parameter model is used to design the Feed Forward series controller for disturbance rejection, [27,28]. ...
... The dynamics of the distributed solar collector field are described by the following system of partial differential equations (PDE) describing the energy balance [27,28]: ...
... Based on the thermal losses, coefficient can be expressed as a third order polynomial, = 0 + 1 ( − ) 3 . The coefficient of heat transmission depends on temperature and oil flow [28]. ...
This chapter deals with the so-called sizing ratio (Rs) of gridconnected photovoltaic (GCPV) systems and the relationship of this parameter with the maximum available power at the output of the PV generator subsystem. Additionally, the optimum value of the aforementioned sizing ratio of a GCPV system is defined as the one that maximizes the yearly energy efficiency in the PV installation considered.
The chapter defines Rs of a GCPV system with static configuration, and, starting from the respective PV generator (PVG) and central inverter models, it proposes a systematic procedure to determine its optimum value. This procedure will be adapted later to address the determination of the sizing ratio in reconfigurable GCPV systems. In fact, the chapter shows systems with adjustable Rs, differentiating between systems that adjust the sizing ratio value by modifying the rated power of the energy-processing subsystem used at each moment (the so-called MIX or Multi-master systems), and systems that adjust the RS value by modifying the nominal power of the PVG connected to each energy-processing system (so-called team-based systems).
Finally, the chapter addresses the issues related to the design of this type of systems, with their required control, and the increase of the maximum available power at the output of the PVG that can be achieved with the use of them versus the use of systems with static configuration or not reconfigurable.
... A schematic of the plant is shown in Fig. 1. For a full description of the plant modeling procedure, see [2], [3]. ...
... The dynamics of the distributed solar collector field is described by the following system of partial differential equa-tions (PDE) that describe the energy balance that occurs in the loop [2], [3]: ...
... The density ρ, the specific heat C and the heat transfer coefficient H t depend on the temperature of the fluid. The coefficient of heat transmission depends on the temperature and oil flow [2]. The model is discretized in the longitudinal dimension of the tube, so the dynamics of each loop can be simulated as a chain of submodels. ...
... The initial design of the PID was for the medium water flow conditions. However, since the PID is a fixed parameter controller, when the plant evolves far from these conditions, the performance deteriorates [56]. Taking this initial design, the parameters were tuned to work properly (without great oscillations) throughout the entire range of operations by a test and error method. ...
... The use of the feedforward helps by rejecting the disturbances. Better performance and a higher degree of robustness when controlling the actual plant are achieved [56]. ...
... From The effective solar radiation estimated by the UKF is a smoother version than the one measured. This can be an advantage on this kind of day, because the measured solar radiation is used and very abrupt changes in the water flow to reject the disturbance may appear [56]. ...
One of the ways to improve the efficiency of solar energy plants is by using advanced control and optimization algorithms. In particular, model predictive control strategies have been applied successfully in their control.
The control objective of this kind of plant is to regulate the solar field outlet temperature around a desired set-point. Due to the highly nonlinear dynamics of these plants, a simple linear controller with fixed parameters is not able to cope with the changing dynamics and the multiple disturbance sources affecting the field.
In this paper, an adaptative model predictive control strategy is designed for a Fresnel collector field belonging to the solar cooling plant installed at the Escuela Superior de Ingenieros in Sevilla. The controller changes the linear model used to predict the future evolution of the system with respect to the operating point.
Since only the inlet and outlet temperatures of the heat transfer fluid are measurable, the intermediate temperatures have to be estimated. An unscented Kalman filter is used as a state estimator. It estimates metal-fluid temperature profiles and effective solar radiation.
Simulation results are provided comparing the proposed strategy with a PID + feedforward series controller showing better performance. The controller is also compared to a gain scheduling generalized predictive controller (GS-GPC) which has previously been tested at the actual plant with a very good performance. The proposed strategy outperforms these two strategies.
Furthermore, two real tests are presented. These tests show that the proposed controller achieves adequate set-point tracking in spite of strong disturbances.
... In addition to temperature regulation [4], a very important task in this type of plant is the correct detection of any type of failure and its characterization. This allows the application of any necessary mitigation, reconfiguration, and correction tasks [5], and facilitates to wind turbines to detect multiple faults based on improved triplet loss. ...
... It is given by the partial differential Eqs. (3) and (4), which describe the energy balances in the pipe and the HTF with spatially distributed variables [4,23]. The model is discretized into 172 segments and an integration time of 0.25 s is used. ...
Detecting and isolating faults in collector fields of solar thermal power plants is a crucial and challenging task. The system variables in the collector area are highly coupled, which can lead to a high misclassification rate. For this reason, it becomes necessary to combine knowledge of systems engineering with machine learning techniques that unravel the complex dynamics that govern the systems using historical data. Furthermore, the performance of a solar thermal plant is highly dependent on solar irradiance which changes during the day and is subject to perturbations caused by clouds and other atmospheric conditions. Detecting the fault requires using techniques that cope with the disturbances in solar irradiance.
In this work, real irradiance profiles with many types of clouds are used. First, a model-based fault detector is applied, obtaining an accuracy of over 89% for all test irradiances. Then, different machine learning techniques are compared: static neural networks with and without decoupling strategy, dynamic neural networks, dynamic neural networks in cascade, classification trees, random forests, radial basis function networks, and self-organizing maps. The combination of neural networks was the only method that obtained a total accuracy of over 73% and F1-scores over 50% for all the test irradiance profiles.
... On the other hand, according to [32], for the existence of the matrix G in (45) it is not necessary to satisfy (44), and establishing relation (45) just requires that z d has full row rank such that (41) is satisfied. That is, if the data is not persistently exciting enough, but z d has full row rank, as obtaining the exact Koopman matrices is generally infeasible; there is no need to have a sufficient order of persistence of excitation for the input signal trajectory. ...
... Other parameters and variables are defined in Table 1. Table 2 presents the measured parameters of the ACUREX distributed solar collector field, and a schematic diagram of the ACUREX solar collector field is shown in Figure 2. It must be noted that in Table 1, the geometric efficiency 0 depends on the hourly angle, solar hour, declination, Julianne day, local latitude and collector dimensions and its calculation method is presented in [44]. However, using a constant mean value for the collectors' efficiency parameter n 0 = K opt 0 in Table 2 can work for a limited period of time and is considered in this example without loss of generality. ...
Non‐linearity is an inherent feature of practical systems. Although there have been significant advances in the control of nonlinear systems, the proposed methods often require considerable computational resources or rely on local linearization around equilibrium points. The Koopman operator is an infinite‐dimensional linear operator that fully captures a system's non‐linear dynamics. However, one of the major problems is identifying a Koopman finite dimensional linear model for a nonlinear system. Initiated by the Willems’ fundamental Lemma, a class of data‐driven control methods has been developed for linear systems without the need to identify the system's matrices. Motivated by these two ideas, a data‐driven Koopman‐based predictive control scheme for non‐linear systems is proposed for unknown disturbed non‐linear systems utilising a finite‐length dataset. Then, considering the uncertainty in the Koopman state variables, a robust data‐driven Koopman predictive control structure is presented. Also, the results led to the design of a data‐driven Koopman predictive control strategy with terminal components to ensure the closed‐loop stability of nonlinear systems. The proposed scheme is tested on the distributed‐parameter model of the ACUREX solar collector field (located at Almería, Spain) to regulate the field outlet temperature around a desired value. Finally, simulation results show the effectiveness of the proposed approach.
... The HTF carries the thermal energy to produce steam, and the electrical energy is generated by a steam turbine. For this plant, Therminol 55 is used as the HTF, whose density ( ) and specific heat capacity ( ) are temperature dependent and are given by [25] ( ( ) ) = 903 − 0.672 ( ), ...
... Here, the global coefficient of thermal losses ( ) depends on the outlet ( ), inlet ( ), and ambient ( ) temperatures, and its expression can be found in [25]. Moreover, is the overall efficiency of the collectors, considering the optical and geometric efficiencies. ...
Parabolic-trough solar collector fields are large-scale systems, so the application of centralized optimization-based control methods to these systems is often not suitable for real-time control. As such, this paper formulates a novel coalitional control approach as an appropriate alternative to the centralized scheme. The key idea is to split the overall solar collector field into smaller subsystems, each of them governed by a local controller. Then, controllers are clustered into coalitions to solve a local optimization-based problem related to the corresponding subset of subsystems, so that an approximate solution of the original centralized problem can be obtained in a decentralized fashion. However, the operational constraints of the solar collector field couple the optimization problems of the multiple coalitions, thus limiting the ability to solve them in a fully decentralized manner. To overcome this issue, a novel population-dynamics-assisted resource allocation strategy is proposed as a mechanism to decouple the local optimization problems of the multiple coalitions. The proposed coalitional methodology allows to solve the multiple local subproblems in parallel, hence reducing the overall computational burden, while guaranteeing the satisfaction of the operational constraints and without significantly compromising the overall performance. The effectiveness of proposed approach is shown through numerical simulations of a 10- and 100-loop version of the ACUREX solar collector field of Plataforma Solar de Almería, Spain.
... La ecuación que describe la segunda etapa corrige la temperatura del fluido en función de la energía neta transportada por el, [9], esta ecuaciones: ...
... El coeficiente de transmisión de calor, formado por una parte dependiente de la temperatura del fluido y otra del caudal de aceite [9,10], se muestra en las ecuaciones 6 y 7 [6]. Para resolver estas ecuaciones, es necesario emplear expresiones complejas de transmisión de calor por convección. ...
Hoy en día, a la hora de estudiar el rendimiento de una planta, es típico contar con un modelo fiable que permita realizar pruebas sin desperdiciar recursos en ensayos innecesarios en la planta real. Uno de los programas empleados en la industria es Matlab®, el cual posee la herramienta llamada Simulink®, con la que se puede describir un sistema mediante diagramas de bloques. En este trabajo se describe el diseño de un modelo en Simulink, de la instalación de investigación de colectores cilindro-parabólicos (CCP) TCP-100, en la Plataforma Solar de Almería (PSA). Este modelo permitirá el aprendizaje didáctico sobre control de plantas termosolares mediante el uso de controladores básicos y muy usados en la industria, así como las ventajas y desventajas del uso de los mismos en plantas altamente no lineales. Los resultados del modelo en Simulink serán comparados con los obtenidos al programar el mismo modelo en el código de Matlab.
... The model of a solar collector loop is described by the following system of partial differential equations (PDE) describing the energy balance ( [27]): ...
... The increased defocus angle decreases the geometric efficiency of the collector, leading to a decrease in fluid temperature [19]. If the temperature rises above 305°C, all collectors are moved to the stow position to avoid overheating situations [27]. Figure 17 shows a test in which the reference to the average plant temperature is 298°C from 12 h onward. ...
The size of existing commercial solar trough plants poses new challenges in applying advanced control strategies to optimize operation. One of these challenges is to obtain a better thermal balance of the loops’ temperature. Since plants are made up of many loops, the efficiency of the loops can vary substantially if a group has been cleaned or affected by dust. This leads to the need to defocus the collectors of the most efficient loops to avoid overheating problems, thus producing energy losses. In order to minimize these energy losses, the input valves have to be manipulated to reduce the temperature difference of the loops. However, when the pipes connecting the loops are very long, the pressure drop and energy losses in those pipes become notorious, affecting the flow distribution. The need to consider the hydraulic model becomes very important. In this paper, a non-linear model predictive algorithm is presented that uses a hydraulic model of the solar field to compute the aperture of the input valves. The results show that when the length of the pipes is increased, the algorithm proposed in this paper obtains better results than other algorithms proposed in the literature.
... This section describes the ACUREX plant [27,28] used for simulation, the two models employed, and the control architecture. The plant has been widely used in the literature and was located at the Plataforma Solar de Almería, Spain. ...
... The model used for simulation is the distributed parameter model (DPM) [27,29], which allows describing the system with spatially distributed variables. Equations (1) and (2) describe the energy balances on the metal and the fluid with the notation given by Table 1, where the subscript m refers to metal and f refers to fluid. ...
Solar plants are exposed to the appearance of faults in some of their components, as they are vulnerable to the action of external agents (wind, rain, dust, birds …) and internal defects. However, it is necessary to ensure a satisfactory operation when these factors affect the plant. Fault detection and diagnosis methods are essential to detecting and locating the faults, maintaining efficiency and safety in the plant. This work proposes a methodology for detecting and isolating faults in parabolic-trough plants. It is based on a three-layer methodology composed of a neural network to obtain a preliminary detection and classification between three types of fault, a second stage analyzing the flow rate dynamics, and a third stage defocusing the first collector to analyze thermal losses. The methodology has been applied by simulation to a model of the ACUREX plant, which was located at the Plataforma Solar de Almería. The confusion matrices have been obtained, with accuracies over 80% when using the three layers in a hierarchical structure. By forcing all the three layers, the accuracies exceed 90%.
... For these approaches, a spatial irradiance map throughout the plant could be very useful. More about the control of thermosolar power plants can be consulted in [7]. ...
... To analyze the performance, several indexes usable for any MRTA algorithm have been defined. They consider different points of view such as distance, energy, dispersion, etc, and their values are represented in Tables 4,5,6,7,8,9,The distance traveled by the vehicles, since distance is one of the parameters used to perform the allocations. ...
In this paper, an event-Mixed Integer Linear Programming (MILP)-based algorithm is proposed to solve the task allocation problem in a Robotic Sensor Network (RSN). A fleet of two types of vehicles is considered, giving, as a result, a heterogeneous configuration of the network, since each type of vehicle has a nominal velocity and a set of allowed paths to go. The algorithm can be applied to the distributed estimation of the solar irradiance on a parabolic trough thermosolar power plant which can be used to increase the global efficiency of the plant. A simulation environment has been built to test the proposed algorithm, taking into account the behavior of the vehicles and the structure of the solar plant. The algorithm has been compared with traditional methods such as the Optimal Assignment Problem (OAP) using a set of indexes that have been defined to this purpose.
... The distributed solar field dynamics can be described by a partial differential equations (PDE) system shown in Eq. (6). The system energy balance is described in this set of PDEs (Carmona, 1985;Camacho et al., 1997;Gallego et al., 2016): ...
... Coefficients and parameters , specific heat and density depends on the temperature of the fluid. Coefficient depends on fluid temperature and HTF flow-rate (Camacho et al., 1997). An approximation for can be obtained from Burkholder et al. (2007) and Lüpfert et al. (2008). ...
In solar thermal plants, as in any industrial process, it is important to maintain good control of the system and, more importantly, to have a good security system to avoid exceeding the safety limits of the components and avoid their degradation. In the case of solar thermal plants, one of the main components is the Heat Transfer Fluid (HTF), which must be kept below a maximum temperature. Although the temperature of the fluid, in general, will be controlled by modifying the flow-rate, when the plant is saturated HTF temperature is kept under limits by defocusing of the collectors.
In this paper, an analysis of the control of the defocus control applied to the different collectors is presented. A Model Predictive Control technique will be applied to control the temperature by defocusing two and four collectors in different situations. It is shown how controlling the temperature by defocusing only two collectors is not sufficient in all situations and that controlling by defocusing the four collectors solves this problem in addition to maintaining the defocus actions in areas with high control authority.
... The thermosolar in the La Africana plant uses a parabolic trough collector (PTC). In this configuration, collectors consist of parabolic reflectors and a tube in the parabola's focal point where heat transfer fluid (HTF), often thermic oil, is heated to produce steam for a turbine [16] [17]. In order to optimize plant efficiency and prevent maintenance issues, the HTF's temperature must be maintained within a specific range. ...
This thesis investigates real-time optimization for solving dynamic target allocation and tracking with heterogeneous robotic systems. The mission of this research is defined by two tasks, dynamic target allocation and dynamic target tracking. In the former task motivated by radiation monitoring over a mega-solar power plant, we will consider the future assignments of the multiple tasks within an allocation horizon while also estimating the task evolution. Moreover, the energetic feasibility of the robot will also be taken into account in the overall online optimization. The algorithm is formulated so that it can be relaxed into an equivalent Linear Programming (LP) problem to reduce the computational cost. On the other hand, for the tracking problem, another optimization algorithm is proposed to increase the robustness of the target tracking by dynamically adjusting the altitude and the object detection model of the aerial robot. The scenario is finally demonstrated through simulation and experiment using ROS (Robotic Operating System) environment and Gazebo simulator.
... This section presents the characteristics of the ACUREX parabolic-trough solar collector field (Camacho et al. (1997)), located in Plataforma Solar de Almería, Spain. We can describe the field as a set L = {1, . . . ...
Coalitional control partitions a system into multiple clusters or coalitions that solve independent local subproblems in parallel. This paper presents a two-layer coalitional model predictive control approach for regulation in constrained-coupled subsystems. We formulate a resource allocation mechanism to distribute the coupled constraint so that the global control problem can be solved in a decentralized manner, guaranteeing the satisfaction of the common constraint. In particular, a top layer will calculate the system's partition according to a given criterion and supervise the shared resource allocation. In turn, the lower control layer will calculate the local optimization problems for every coalition in a decentralized fashion, according to the available shared resource determined by the upper layer. This strategy is applied to regulate the outlet temperature of parabolic-trough solar collector fields, which are composed of a set of loops that remain coupled through a global shared resource constraint.
... Here, we study the ACUREX parabolic-trough solar collector field located in Plataforma Solar de Almería (PSA), Spain. For this plant, Therminol 55 is used as the HTF, whose density (ρ) and specific heat capacity (c) are given by (Camacho et al., 1997): ρ (T f (t)) = 903−0.672T f (t) and c (T f (t)) = 1820 + 3.478T f (t), where T f (t) is the temperature of the HTF at time instant t. ...
This paper proposes a coalitional model predictive control method for temperature regulation in parabolic-trough solar fields. The global optimization problem is divided into a set of local subproblems that will be solved in parallel by a set of coalitions. However, these local (smaller) problems remain coupled by a common global resource constraint. In this regard, we present a population-dynamics-assisted resource allocation approach to fully decouple the local optimization problems. By doing this, each coalition can address its corresponding optimization problem without relying on the solutions of the other coalitions. To illustrate the proposed methodology, we provide simulation results for a 100-loop parabolic-trough solar collector field.
... The system is described with the distributed parameter model given by Eqs. (A.1) and (A.2), which represent the energy balances in the fluid and the pipe using spatially distributed variables [28,29]. It is a well-known model validated by Camacho et al. [30] that has been used by researchers in many different studies such as the works by Masero et al. [31], and Gholaminejad and Khaki-Sedigh [32]. ...
Fault detection is crucial for ensuring optimal operation and maintenance of solar plants. This paper proposes a methodology for fault detection and isolation using artificial neural networks (ANNs) in a model of a 50 MW parabolic-trough solar plant that employs a defocusing strategy. The proposed methodology focuses on detecting three different types of faults in the collector area, namely, faults in the optical efficiency, flow rate, and thermal losses. The methodology is divided into three steps. Firstly, a feedforward dynamic neural network that internally models the concentrated parameter model of the system is used to detect faults and output the fault type. Secondly, information on the defocusing mechanism is added to the inputs of the neural network. Finally, the range of faults considered is adjusted based on the neural networks’ ability to detect each fault size and its impact on the plant’s outlet temperature. The accuracy of fault detection is evaluated through several simulations, and the proposed methodology shows promising results. The accuracy of fault detection is found to be 71.72%, 83.96%, and 90.62% for the first, second, and third approaches, respectively. The proposed methodology based on ANNs has the potential to improve the operational efficiency and reduce maintenance costs of solar plants.
... In the referenced article [3], an adaptive internal model controller that makes use of the MIT rule-adjustment mechanism is described. Although the MIT rule yields the best results, it does not guarantee convergence or stability [4]. ...
Sustainable energy sources are valuable energy sources.Renewable energy production boosts the economic status of a country. Wind energy is one of the most abundant renewable energy sources, and as a result, the technology to harvest energy from the wind is growing rapidly around the world. As load centers are far away from renewable energy sources, electricity must be transferred over long distances. The most common problems with power fluctuations are caused by long-range voltage sag riding (VSRT). DFIG (Doubly Fed Induction Generator) is very popular in wind energy conversion systems due to its variable speed, high energy collection, efficiency, excellent design and unique control of phase side converters and rotor side converters. Wind speed monitoring is done with the help of Internet of Things (IOT). This paper described the use of training networks in developing adjustment algorithms for direct reference model adaptive IMC for DFIG wind farms. Here, a novel training-based neural network MIT (NNMIT) adjustment mechanism using neural network method is developed and implemented in direct reference model adaptive IMC to improve the performance of the controller during voltage sag. Direct and quadrature axis rotor current controllers are developed and the resulting DFIG is balanced with the FuzzyMIT correction mechanism in the sag ride through the conditions in the wind farm. Improvements across the voltage sag are identified and presented using NNMIT. The proposed NNMIT attain 0.15% torque ripple and 1 ms response time better than the existing FuzzyMIT method. The proposed method preserves high accuracy ranges of 97.88% than the existing method. This approach gives better performance than other control design methods which assume that the flux in the stator is constant in amplitude.
... Silva et al. [11] presented a study on HTF as a therminol oil temperature profile of PTC that was modeled using a transport equation and solution obtained using the method of characteristics. Camacho et al. [12] described several control system designs using data-driven models for PTC. ...
Electricity generation from solar energy has become very desirable because it is abundantly available and eco-friendly. Mathematical modeling of various components of a Solar Thermal Power plant (STP) is warranted to predict the optimal and efficient operation of the plant. The efficiency and reliability of STPs are maximized based on different operating strategies. Opting for proper Heat Transfer Fluid (HTF), which is proposed in this paper, helps in reducing operating complexity and lowering procurement cost. The Parabolic Trough Collector (PTC) is the heart of STP, where proper focusing of PTC towards solar radiation is the primary task to maximize the outlet temperature of HTF. This maximum temperature plays a major factor due to diurnal solar radiation variation, and its disturbance nature, with the frequent startup and shutdown of STP, is avoided. In this paper, the PTC component is modeled from the first principle, and, with different HTF, the performance of PTC with constant and quadratic solar disturbances is analyzed along with classical control system designs. Through this, the operator will be able to choose proper HTF and resize the plant components depending on plant location and weather conditions. Furthermore, the thermal energy is collected for therminol oil, molten salt, and water; and its performance with different inputs of solar radiation is analyzed along with closed-loop controllers. Thermal energy extracted by therminol oil, molten salt, and water with constant solar radiation results in 81.7%,73.7% and 18.7%, respectively.
... The distributed parameter model [18], [19] is used for simulating the plant. It describes the system with energy balances on the metal and the fluid circulating through the pipes. ...
Solar plants are exposed to numerous agents that degrade and damage their components. Due to their large size and constant operation, it is not easy to access them constantly to analyze possible failures on-site. It is, therefore, necessary to use techniques that automatically detect faults. In addition, it is crucial to detect the fault and know its location to deal with it as quickly and effectively as possible. This work applies a fault detection and isolation method to parabolic trough collector plants. A characteristic of solar plants is that they are highly dependent on the sun and the existence of clouds throughout the day, so it is not easy to achieve methods that work well when disturbances are too variable and difficult to predict. This work proposes dynamic artificial neural networks (ANNs) that take into account past information and are not so sensitive to the variations of the plant at each moment. With this, three types of failures are distinguished: failures in the optical efficiency of the mirrors, flow rate, and thermal losses in the pipes. Different ANNs have been proposed and compared with a simple feedforward ANN, obtaining an accuracy of 73.35%.
... It should be noted that control strategies can be applied in any of the integration concepts reported in Section 2.3 as the management can be achieved by merely varying the flow rate or the aperture of the different valves available in the plants [78,79]. ...
The use of solar thermal systems to produce heat for industrial processes is a feasible option that is gaining increasing interest in recent years as an initiative toward the zero-carbon energy future. This technology has a place in different processes, yet there is still no consensus on the main methods for sizing or controlling. The design requires the use of specific techniques due to the inconstant nature of solar energy as well as the heterogeneity of some industrial thermal demands. Nevertheless, despite starting from a particular system’s design, the dynamic together with the hybrid and nonlinear behavior of the processes involved require adequate control techniques to provide the energy in a usable form and keep the system operating close to the design specifications. This paper presents a literature review concerning research works that address the design and control of solar thermal systems used in industrial contexts. The main objective is to analyze the different techniques used and to highlight their limits, usefulness, and the various industrial sectors where they were applied. The results of this analysis can be seen as a decision-making tool to select the most appropriate design or control strategy for these applications. It has been found that control techniques such as model predictive control can improve key performance metrics in daily operation. However, further development on these kinds of techniques and in holistic optimization methods that exploit the synergies between the operational and design phases is required.
... El esquema de control puede verse en la Figura 4 y funciona así: cada 36 segundos, el sistema de adquisición de datos recibe las medidas del campo, las cuales son usadas por el observador para obtener una estimación del vector de estados. Posteriormente, el sistema actualiza las matrices del modelo lineal en el espacio de estados y computa la respuesta libre usando un modelo de parámetros distribuidos no lineal (Carmona, 1985;Camacho et al., 1997). Porúltimo se resuelve el problema de control preditivo planteado en el sistema de ecuaciones (2). ...
Uno de los principales métodos para mejorar la eficiencia en el uso y aprovechamiento de la energía solar es la aplicación de técnicas avanzadas de control. En este trabajo se presenta un estado del arte de las aplicaciones de control predictivo en plantassolares de pequeña y gran escala. Se presentan además dos aplicaciones reales: una que se diseño para la planta experimental ACUREX en la Plataforma solar de Almería (PSA). El controlador fue probado en el campo real con buen desempeño. La otra aplicación describe el diseño de un controlador predictivo para plantas comerciales de colectores cilindro parabólicos (CCP) que está instalado en 13 plantas Españolas así como en las plantas de Mojave en California (USA). Se muestran dos resultados reales obtenidos en la planta Mojave Beta con el controlador propuesto.
... Unlike traditional energy sources such as coal, oil, or hydropower, solar energy cannot be manipulated directly, therefore the study of control techniques is appealing to increase the feasibility of this type of process [1,2]. Concentrating Solar Power (CSP) plants are one of the processes that can benefit from advanced control techniques like Model Predictive Control, as they must operate under great solar irradiance variations throughout the day [3]. ...
This paper presents the development of a simplified analytical optical model, an analysis of aiming strategies and a defocusing strategy for a Fresnel solar collector. The model aims to represent the main components of solar concentration on a Fresnel collector when considering independent rotation on each mirror in order compute the solar radiance flux on the absorber. The model is validated with data from a reference Ray-Tracing software and from experimental data. The model is then used to evaluate three aiming proposals with regards to solar radiance flux over the absorber. Data from the optical model and mirror inclinations proposed by the aiming strategies are used to implement a partial defocusing strategy for Fresnel collectors. The main purpose of this analysis is to assess the possible gains that individual manipulation of each collector mirror can bring to the process. It is shown that all the analyzed aiming methods present similar performances regrading the optical efficiency aspect. Thus, the use of the simplest solar aiming method presents itself as a very effective solution. Results also show the possibility to implement partial defocusing on Fresnel collectors, allowing the use of focus as a manipulated variable for control.
... De esta forma, cuando hay menos energía solar que aprovechar, el sistema disminuye el caudal bombeado para que el FC alcance mayor temperatura (dentro de los límites operacionales de la planta). Sin embargo, en ocasiones la irradiancia no es homogénea, por ejemplo, como consecuencia de una nube que cubre parcialmente el campo, por lo que una distribución equitativa del FC entre los lazos puede provocar un excesivo aumento de temperatura en las zonas no sombreadas, lo que puede obligar a desenfocar los espejos para evitar daños físicos en la planta (Camacho and Berenguel, 2012;Camacho et al., 1997). ...
El control predictivo engloba a una familia de controladores que replanifican continuamente las entradas del sistema durante un cierto horizonte temporal con el fin de optimizar su evolución esperada conforme a un criterio dado. Esta metodología tiene entre sus retos actuales la adaptación al paradigma de los llamados sistemas ciberfísicos, que están compuestos por computadoras, sensores, actuadores y entidades físicas de diversa índole entre las que se incluyen robots e incluso seres humanos que intercambian información con el objetivo de controlar procesos físicos. Este tutorial presenta los conceptos centrales de la integración del control predictivo en este tipo de sistemas mediante el repaso a una serie de ejemplos que explotan la versatilidad de este marco de diseño de controladores para resolver los desafíos que presentan las aplicaciones del siglo XXI.
... The flat-plate solar collector field is the primary energy source of the CIESOL plant. The model proposed here is a simplified lumpedparameter dynamical model [7]. Equation (1) depicts the solar collector hybrid model. ...
Solar thermal plants are commonly constituted of different subsystems, such as the solar collector field, accumulation tanks, and gas heaters, to enhance plant performance. Nevertheless, from a process control perspective, the changes in the subsystem's configuration can cause interactions and compromise the controller performance. Therefore, this work proposes a hybrid practical nonlinear predictive control for a solar thermal plant facility, aiming to improve the plant operation performance by considering mixed-logical dynamic models and including the process constraints as linear mixed-integer inequalities in a single control layer. The proposed strategy is compared with the conventional practical nonlinear control with an external decision-maker. Both frameworks are simulated under actual process circumstances using real meteorological data and validated hybrid models of the CIESOL facility (Spain). This paper makes relevant contributions in some aspects. From the control framework perspective, a new formulation of the hybrid control algorithm simplifies the control structure, excluding the decision-maker structure from its task. Moreover, the present study considers variations in load demand, a novel and significant contribution. The results demonstrate that the hybrid nonlinear controller performs better than the conventional approach, with a reference tracking error index approximately 35% lower in a sunny day scenario.
... Based on this model, an optimization problem is solved to obtain the best future control signals by minimizing a cost function. MPC has been successfully applied to this plant on several occasions [15,16,33]. In this work, we implement an MPC controller, whose inputs and outputs will serve to train an artificial neural network. ...
In solar parabolic-trough plants, the use of Model Predictive Control (MPC) increases the output thermal power. However, MPC has the disadvantage of a high computational demand that hinders its application to some processes. This work proposes using artificial neural networks to approximate the optimal flow rate given by an MPC controller to decrease the computational load drastically to a 3% of the MPC computation time. The neural networks have been trained using a 30-day synthetic dataset of a collector field controlled by MPC. The use of a different number of measurements as inputs to the network has been analyzed. The results show that the neural network controllers provide practically the same mean power as the MPC controller with differences under 0.02 kW for most neural networks, less abrupt changes at the output and slight violations of the constraints. Moreover, the proposed neural networks perform well, even using a low number of sensors and predictions, decreasing the number of neural network inputs to 10% of the original size.
... C. Ponce et al. [8] designed a dynamic simulator for an ISCC power plant using MATLAB Simulink ® , based on a solar power plant simulator and a CCPP simulator developed by E. Camacho et al. [9] and D. Sáez et al. [10], respectively. They combined their dynamic simulator with a supervisory control strategy regulating the steam pressure of the superheater (SH) to account for the fuel savings that could be achieved when integrating solar collectors with a CCPP. ...
The combined cycle power plants are the most recognized thermal power plants for their high efficiency, fast start-up capability, and relatively low environmental impact. Moreover, their flexible unit dispatch supports the share of renewable energy, which contributes to carbon mitigation. The operational flexibility of Integrated Solar Combined Cycle (ISCC) power plants is a crucial factor for reliable grid stability. To evaluate the limitations and capabilities of ISCC power plants and their control structures, dynamic simulation is a feasible method. In this study, a sophisticated dynamic process model of the ISCC power plant in Kuraymat, Egypt, has been developed using APROS software. The model describes the plant with a high level of detail including the solar field, the heat recovery steam generator, and the control structures. The model was implemented structurally identical to the reference plant and tuned using the operational design data. Actual measurements were used as the basis for the initialization and validation of the dynamic simulation environment. Dynamic analysis of four different days was performed, then the simulation results were presented and compared with actual measurements. The comparison showed that the course of the actual measurements could be predicted with high accuracy. The solar field influences and the system’s overall power curves are reliably simulated. Consequently, the validated model can simulate the dynamic behavior of the ISCC power plant with a high degree of accuracy, and can be considered in future planning decisions.
... The oil temperature and pipe walls were modeled separately. The sun situation and geometrical situation of collector field, capability of mirror reflection, and the input oil temperature also are considered in the model [5] and [4]. The collector lope was the most important subsidiary system which sets the behavior of collector field. ...
In this article the linear parabolic solar power plant has been modeled via system identification techniques and non linear model of oil cycle and its equations. A combination of actual and simulationdata have been employed for this task.Moreover different models for the system have been presented and studied. In the proposed model the input and output have been assumed to be the entering oil flow and out going oil temperature. Therefore performance cycle and parameters have been analyzed, according to available data from Shiraz solar power plant.
... It should be noted that the transfer function uses for analysis of dynamic behavior for the solar collector is partly pioneer, although it is not widely employed. More precisely, due to the intricate system governing equations more advanced controllers such as non-linear control (NC), fuzzy logic control (FLC) and neural network controllers (NNC) could be applied, however, this attitude, has trivial complex computational formulation and more convenient implementation in conflict to NC, FLC and NNC approach [31] and linear control procedure like proportionaleintegralederivative controller [32] or more robust controller can be used accordingly [33]. The present paper proposes a new method: Combined Energy-Exergy-Control (CEEC) for the analysis of the solar power generation system. ...
eThe achievement of a renewable plant with maximum efficiency (energy and exergy) and a suitable control system (with minimum settling time) is one of the most critical issues in the design and optimization of power plants. Accordingly, in this research, a novel method: Combined Energy-Exergy-Control (CEEC) method is used for the analysis and a new steam power generation system that supplies all the required heat by a set of parabolic trough collectors is used as a case study. A wide range of researches regarding the power cycles separately studied the problem of efficiency improvement without control considerations or an efficient control system without performance considerations. Therefore, in this paper, the problem of achieving an efficient thermodynamic system with appropriate control characteristics was considered using the CEEC method. To this end, the energy and exergy analysis was performed for the proposed cycle, followed by accurately modeling the parabolic trough collectors (PTCs). Then, the governing control equations are presented, and the response time of the control system is determined accordingly. Finally, CEEC optimum configuration is proposed using multi-objective optimization to maximize the energy/exergy efficiency and minimizing the settling time of the proposed cycle. The two-objective optimization outcomes indicated 36.06 and 25.09% improvement in whole cycle energy performance and settling time, and the three-objective optimization (energy-exergy-settling time) stands for 34.02, 28.25 and 17.63% enhancement in target function respectively.
... Controlling the heliostat field of an SPT plant encompasses from selecting the set of active heliostats to defining their aim points to keep the desired flux profile at any time [11]. Not only the apparent solar movement and variations in direct normal irradiance must be considered but also modeling and physical errors as well as cloud transients. ...
The competitiveness of Concentrated Solar Power (CSP) plants over conventional ones still has to be improved. For CSP systems based on Central Receivers (CRS), one of the challenges to face is the optimal management of the aim points of the heliostats which form the collector field. The flux distribution that the field projects on the receiver must be carefully controlled to get an adequate form and to avoid dangerous flux peaks that might damage the receiver. Phenomena such as cloud transients can result in pronounced temperature gradients that reduce the life expectancy of receivers. Therefore, it is necessary to develop a control system which ensures that the critical parameters of the receiver (e.g., temperatures, solar radiation, pressure, mass flow) are always within the allowed range. This work presents an automatic control system connected to an optimization method based on a genetic algorithm which theoretically configures the field to obtain any desired flux distribution. It is a heuristic feedback controller that minimizes the error between the flux distribution theoretically computed and that obtained over time. The control logic tries to reduce the effect of perturbations as well as modeling and optimization errors that might have affected the genetic optimizer when computing the initial operating state.
... Considerable research effort has been developed during the last 30 years concerning the modeling and control of solar power plants. The first works related to the modeling and control were developed for the experimental solar trough plant ACUREX [7][8][9][10][11]. Then, many works concerning the mathematical modeling of solar trough plant have been published, going from first principle models [12,13], where heat transfer equations are used [14], to object oriented modeling using software such as modelica [15]. ...
Competitiveness of solar energy is one of current main research topics. Overall efficiency of solar plants can be improved by using advanced control strategies. To design and tuning properly advanced control strategies, a mathematical model of the plant is needed. The model has to fulfill two important points: (1) It has to reproduce accurately the dynamics of the real system; and (2) since the model is used to test advanced control strategies, its computational burden has to be as low as possible. This trade-off is essential to optimize the tuning process of the controller and minimize the commissioning time. In this paper, the modeling of the large-scale commercial solar trough plants Mojave Beta and Mojave Alpha is presented. These two models were used to test advanced control strategies to operate the plants.
... 17,18 Notably, a reference book 19 deals essentially with the handling of the solar intensity fluctuations on an automatism point of view, on the basis of constant output temperature for the control of large-scale steam turbines, with or without thermal storage. Since then, it has been shown, by the use of exergy approach that constant output temperature is not optimal, either for steam plants 20 or other plants. [21][22][23][24][25] The latest references present exergy optimizations of solar plants based on the available exergy flow only. ...
In this investigation, exergy‐based optimization was carried out on two configurations of solar thermal collectors. A set of control sensors were utilized to control the performance of the system and monitor the design variables, that is, inlet and outlet temperature of the solar fluid, ambient temperature, incident solar flux, and required mechanical and electrical work in the system. The fluid temperature was considered to vary in the range of 120–180°C with respect to technical and economic considerations. The collector surface area was considered 606 m² in both configurations, and a medium solar incident was considered about 520 W/m². The maximum acceptable temperature difference is obtained as function of the input temperature. The technical limitation is plotted for both configurations of solar collectors. It was noticed that the exergy flow of simple evacuated tubes can reach the maximum value when the input temperature is greater than 115°C for an incident solar flow, from 250 W/m² up to the limit of 950 W/m². Exergy analysis demonstrated that more control is required on the temperature control to obtain the optimum performance of the solar collector. Thus, applying exergy optimization approach can be very useful to guide control system, the maximum outlet temperature being a key variable on the performance of the collectors.
... A heat transfer fluid, which is usually a synthetic oil, flows through the receiver tube to absorb solar radiation. Once the heat transfer fluid is heated, it is transferred to the heat exchanger to generate high-temperature steam, which is used to generate electricity via a steam turbine (For further details see [3,8,9,15,18]). For a maximal efficiency of the heat exchanger, the temperature of the heat transfer fluid has to be close to a required level T d 400 • C. The fluid temperature depends on the intensity of solar radiation, the geometry and the optical properties of the reflectors, the thermal properties of the HCE, and on the fluid velocity. In order to reach the required temperature, it is imperative to determine the optimal fluid velocity. ...
This paper examines an optimal control problem for a parabolic trough receiver. We consider a simplified bilinear distributed model, where the control stands for the velocity of the heat transfer fluid. Using the tools of semigroup theory, we characterize the optimal control, that steers the fluid temperature close to the required level. Then we give an algorithm for the numerical implementation of the optimal control. The obtained results are illustrated through simulations.
Solar thermal plants have high nonlinearities and non-manipulated energy source which make their control task a very challenging work. Linear controllers cannot cope with undesirable deviations of the outlet temperature over all the operation range of the dynamics of this type of plants. Moreover, nonlinear predictive control relying on online nonlinear optimization have the drawback of time consuming and numerical calculus issues. In this paper, an infinite gain scheduling neural predictive control is designed and applied to control the temperature in a distributed parabolic trough solar collector field. The performance of both tracking and disturbance rejection of the proposed controller is compared to those of four nonlinear predictive control variants: Two unconstrained neural predictive control using the Levenberg–Marquardt and the Broyden–Fletcher–Goldfarb–Shanno algorithms, and two constrained nonlinear predictive control using interior point algorithm, one is based on a neural network model and the other one is based on a first principal model. The superiority of the proposed control strategy is well demonstrated through simulation results.
Environmental, economic and strategic reasons are behind the rapid impulse in the deployment of renewable energy sources that is taking place around the world. In addition to overcoming economic and commercial barriers, meeting the ambitious objectives set by most countries in this field will require the development of novel technologies capable of maximizing the energy potential of different renewable sources at an acceptable cost. The use of solar radiation and biomass for power generation is growing rapidly, particularly in areas of the globe where these resources are plentiful, like Mediterranean countries. However, solar energy plants necessarily suffer from the intermittency of day/night cycles and also from reduced irradiation periods (winter, cloudy days, short transients). Biomass power plants have to confront the logistic problems associated with the continuous supply of very large amounts of a relatively scarce and seasonal fuel. Hybrid systems may provide the solution to these limitations, maximizing the energy potential of these resources, increasing process efficiency, providing greater security of supply and reducing overall costs. This paper focuses on the brief discussion about solar thermal, solar biomass, pyrolysis process, products and their characteristics.
Predictive control strategies with implicit feedforward action are known for enhancing solar collector field system performance. Nevertheless, the nature of the systems’ disturbances, such as solar irradiance, is characterized mainly as being stochastic, which compromises the disturbance rejection performance due to the model prediction uncertainties. Therefore, this work proposes a stochastic model predictive control (MPC) based on a chance-constraint (CC) formulation for controlling a real solar thermal plant. The controller is presented as a CC practical nonlinear MPC (CC-PNMPC), and it is implemented in the AQUASOL-II facility located at Plataforma Solar de Almería, Almería, Spain. This work first investigates the solar collector field plant model based on a parameter identification framework and the irradiance model predictions, using three different models for forecasting. After studying the benefits of the CC-PNMPC in distinct simulated scenarios, which presented about 7% less error out of the output limits than the deterministic strategy, the stochastic controller is implemented in the actual AQUASOL-II facility to validate and demonstrate the advantages of the proposed control approach. The results show that the stochastic strategy can straightforwardly account for disturbance uncertainties in the control optimization layer without additional computational cost or mathematical efforts. Furthermore, for the irradiance prediction uncertainties case, simulations demonstrate that the CC-PNMPC systematically reduces the temperature threshold extrapolation compared to the deterministic strategy.
Distributed solar collector fields are an interesting case in the area of control since apart from presenting several disturbances, their main source of energy is solar irradiation that cannot be manipulated, depends on daily and seasonal variations causing the solar resource is not always available. This makes it necessary to research control techniques that can optimize the use of the existing solar resource. Therefore, in this research, a model predictive control strategy MPC is designed for a virtual distributed solar collector field where the control objective is to maintain the output temperature of a fluid at the desired value. In addition, a PID control strategy with a Feedforward block is also implemented that it's used to compare the results obtained with the MPC to determine which controller has better performance, which one allows a longer operation time, optimizing the use of the available solar irradiation and which one responds better to disturbances. All this is through an immersive virtual environment where the user can interact with all the instrumentation of the virtual distributed solar collector field and can visualize the evolution of the variables and modify the state of the virtual plant by manipulating the disturbances as well as the parameters of the PID and MPC controllers designed. Finally, the results show a better performance of the plant when implementing the MPC control strategy and the advantages of implementing a virtual environment interactive with the user.KeywordsModel Predictive ControlSolar Collector FieldVirtual Industrial Process
Solar Direct Steam Generation (DSG) systems are well suited for process steam applications and are able to provide steam at the pressure required by common industrial processes. Nevertheless, reliable control has always been a challenge for solar DSG system hindering its wider adoption. In this paper, a control strategy for solar DSG systems is presented. The control strategy is based on PID control theory combined with model-based feedforward control. Experimental data demonstrate that the control strategy provides good performance in terms of stability and setpoint tracking. The error in setpoint tracking for the load pressure controller is shown to be as low as 0.005 MPa under real life conditions. The said strategy is currently implemented in two commercially operating plants providing solar steam for industrial processes.
En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264. [2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September. [3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344. [4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall. [5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico. [6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141. [7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266. [8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática. [9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.
It is challenging and crucial to achieve unbiased tracking control for parabolic trough collector field as it is vulnerable to various types of disturbances or uncertainties such as unmeasured external disturbances, parameter perturbation and model mismatch. To solve this issue, an optimal model predictive rejection control strategy is put forward in a composite designed manner, in which all disturbances/uncertainties are dealt with as lumped disturbances. A generalized extended state observer is firstly employed to estimate the lumped disturbances, and then a feedback controller is devised based on optimal model predictive control to compensate the influences of the lumped disturbances on output. Stability analysis of the closed-loop system has been presented. It shows that the proposed composite controller can track given references without offset in the presence of lumped disturbances while not sacrificing its nominal performance in the absence of disturbances. Simulations conducted on a numerical example and a practical application for parabolic trough collector validate our conclusions.
Modulating functions method is a non asymptotic estimation method, which provides accurate and robust estimations of states, parameters and inputs for different classes of systems, which include unknown linear ordinary differential systems, fractional systems and linear partial differential equations. In the case of time or space varying unknown, the method requires the decomposition of the unknown into predefined basis functions. However, the estimation performance will depend on the nature of the basis functions which in some cases are not easy to determine. This paper proposes a new iterative learning based modulating functions method, which combines the standard modulating functions with a dictionary learning procedure. The dictionary learning step allows the determination of appropriate set of functions to decompose the unknown, while the modulating function step allows the non-asymptotic and robust estimation of the projection coefficients. The performance of the proposed method is illustrated in a distributed solar collector application, modeled by partial differential equations and where the unknown solar irradiance is estimated.
Parabolic trough solar thermal power plants use a thermal fluid to transfer thermal energy from captured solar radiation to a Rankine cycle in order to drive a turbine that, coupled to an electrical generator, produces electricity. Continuous improvement of their performance will lead these technologies to truly compete with either conventional or other forms of renewable energy systems. Nanofluids have been proposed as a way to improve heat transfer rates in thermal solar plants. Hence, nanofluids thermal solar plants coupled with dynamic optimal operating policies should render improved heat transfer characteristics as well as power production. The main objective of this work is to obtain optimal start-up policies of such plants using diverse nanofluids (Al2O3-water and TiO2-water) compared against pure water as base fluid, focused on power delivery and the time required for the system to reach target conditions. For the analysis, the deterministic dynamic mathematical model of a previously proposed solar thermal power facility was extended to deploy nanofluids for improved capture of solar radiation. Both nonlinear programming and mixed-integer nonlinear programming optimization formulations, consisting on energy and mass balances, were deployed for the comparison of various start-up scenarios when alternating between nanofluids and pure water as the working fluid on the solar collector. Results indicate that the TiO2-water nanofluid is the most suitable heat transfer fluid among the ones studied for improved heat transfer recovery, and that dynamic optimal control operating policies aids the system to reach better operational conditions rather than when following simple heuristic-based start-up policies.
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