Wind turbine control using T-S systems with nonlinear consequent parts
TL;DR: A novel T-S model with nonlinear consequent parts is introduced for the variable speed, variable pitch wind turbine and a robust H ∞ observer based fuzzy controller is designed to control the turbine using the estimated wind speed.
About: This article is published in Energy.The article was published on 2019-04-01. It has received 32 citations till now. The article focuses on the topics: Turbine & Wind speed.
Citations
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TL;DR: A nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper.
Abstract: With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can improve the dynamic economy performance of the system. However, the variable-speed wind turbine (VSWT) system widely used is generally nonlinear and highly coupled nonaffine systems, containing multiple economic terms. Therefore, a nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper. Three groups of simulations verify the effectiveness and reliability/practicability of the proposed nonlinear EMPC strategy.
42 citations
TL;DR: Comparison results clearly demonstrate that effective wind speed estimated by the proposed method is more accurate than that by the Kalman filter-based method and that the computational efficiency is higher.
26 citations
TL;DR: Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.
Abstract: In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.
24 citations
Cites methods from "Wind turbine control using T-S syst..."
...In [16], a robust H∞ observer-based fuzzy controller is designed to control the turbine using the estimated wind speed....
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TL;DR: In this paper, the authors present configuraciones of control inteligente, basadas principalmente en redes neuronales and aprendizaje por refuerzo, aplicadas al control of las turbinas eolicas.
Abstract: El control del angulo de las palas de las turbinas eolicas es complejo debido al comportamiento no lineal de los aerogeneradores, y a las perturbaciones externas a las que estan sometidas debido a las condiciones cambiantes del viento y otros fenomenos meteorologicos. Esta dificultad se agrava en el caso de las turbinas flotantes marinas, donde tambien les afectan las corrientes marinas y las olas. Las redes neuronales, y otras tecnicas del control inteligente, han demostrado ser muy utiles para el modelado y control de estos sistemas. En este trabajo se presentan diferentes configuraciones de control inteligente, basadas principalmente en redes neuronales y aprendizaje por refuerzo, aplicadas al control de las turbinas eolicas. Se describe el control directo del angulo de las palas del aerogenerador y algunas configuraciones hibridas de control. Se expone la utilidad de los neuro-estimadores para la mejora de los controladores. Finalmente, se muestra un ejemplo de aplicacion de algunas de estas tecnicas en un modelo de turbina terrestre.
21 citations
TL;DR: Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances, and an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system.
Abstract: This work focuses on the control of the pitch angle of wind turbines. This is not an easy task due to the nonlinearity, the complex dynamics, and the coupling between the variables of these renewable energy systems. This control is even harder for floating offshore wind turbines, as they are subjected to extreme weather conditions and the disturbances of the waves. To solve it, we propose a hybrid system that combines fuzzy logic and deep learning. Deep learning techniques are used to estimate the current wind and to forecast the future wind. Estimation and forecasting are combined to obtain the effective wind which feeds the fuzzy controller. Simulation results show how including the effective wind improves the performance of the intelligent controller for different disturbances. For low and medium wind speeds, an improvement of 21% is obtained respect to the PID controller, and 7% respect to the standard fuzzy controller. In addition, an intensive analysis has been carried out on the influence of the deep learning configuration parameters in the training of the hybrid control system. It is shown how increasing the number of hidden units improves the training. However, increasing the number of cells while keeping the total number of hidden units decelerates the training.
21 citations
References
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01 Feb 2009
TL;DR: In this article, 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 is described.
Abstract: 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.
4,194 citations
Book•
01 Nov 2011
TL;DR: Modeling, simulation and control of a doubly-fed induction machine controlled by a back-to-back converter for Doubly-Fed Induction Generators and Three-Phase Power Converters.
Abstract: Analysis of Electric Machinery and Drive SystemsModeling and Control of AC Machine using MATLAB®/SIMULINKModeling, simulation and control of a doubly-fed induction machine controlled by a back-to-back converter[Model Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power ConvertersHigh Performance Control of AC Drives with Matlab/SimulinkGreen EnergyModeling, Control and Analysis of a Doubly Fed Induction Generator Based Wind Turbine System with Voltage RegulationModel Predictive Control of Wind Energy Conversion SystemsModeling of Turbomachines for Control and Diagnostic ApplicationsModel Predictive Control for Doubly-Fed Induction Generators and Three-Phase Power ConvertersFrom Dynamic Modeling to Experimentation of Induction Motor Powered by Doubly-Fed Induction Generator by Passivity-Based ControlWind Driven Doubly Fed Induction GeneratorDynamics and Control of Electric Transmission and MicrogridsModeling and Modern Control of Wind PowerInduction Machines HandbookHigh Performance Control of AC Drives with Matlab / Simulink ModelsModeling and Analysis with Induction Generators, Third EditionPower Conversion and Control of Wind Energy SystemsModeling, Simulation and Control of Doubly-Fed Induction Machine Controlled by Back-to-Back ConverterDoubly Fed Induction GeneratorsWind FarmModeling and Control of AC Machine using MATLAB®/SIMULINKDoubly Fed Induction MachineModeling and Analysis of Doubly Fed Induction Generator Wind Energy SystemsAdvanced Control of Doubly Fed Induction Generator for Wind Power SystemsAdvanced Modeling and Analysis of the Doubly-fed Induction Generator Based Wind TurbinesPower Electronics for Renewable Energy Systems, Transportation and Industrial ApplicationsDoubly Fed Induction Generators2019 4th World Conference on Complex Systems (WCCS)Advanced Control of Doubly Fed Induction Generator for Wind Power SystemsModeling, Analysis, Control and Design Application Guidelines of Doubly Fed Induction Generator (DFIG) for Wind Power ApplicationsModeling, Identification and Control Methods in Renewable Energy SystemsAdvances in Systems, Control and AutomationRenewable Energy Devices and Systems with Simulations in MATLAB® and ANSYS®2018 IEEE PES/IAS PowerAfricaModeling of Wind Turbines with Doubly Fed Generator SystemAnalysis of Sub-synchronous Resonance (SSR) in Doubly-fed Induction Generator (DFIG)-Based Wind FarmsDoubly Fed Induction MachineWind Energy Generation: Modelling and ControlDoubly-fed Induction Generator Based Wind Power Plant Models
766 citations
TL;DR: Experimental results and comprehensive comparison analysis have demonstrated the superiority of the proposed MSCNN approach, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise.
Abstract: This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.
532 citations
TL;DR: The question of theoretical stability of the torque controller is addressed, showing that the rotor speed is asymptotically stable under the torque control law in the constant wind speed input case and L/sub 2/ stable with respect to time-varying wind input.
Abstract: This article considers an adaptive control scheme previously developed for region 2 control of a variable speed wind turbine. In this paper, the question of theoretical stability of the torque controller is addressed, showing that the rotor speed is asymptotically stable under the torque control law in the constant wind speed input case and L/sub 2/ stable with respect to time-varying wind input. Further, a method is derived for selecting /spl gamma//sub /spl Delta/M/ in the gain adaptation law to guarantee convergence of the adaptive gain M to its optimal value M*.
488 citations
01 Jan 2006
TL;DR: In this article, the authors used the Controls Advanced Research Turbine (CART) as a model for this article's research, which is located in Golden, Colorado, at the U.S. National Renewable Energy Laboratory's National Wind Technology Center.
Abstract: 1066-033X/06/$20.00©2006IEEE W ind energy is the fastest-growing energy source in the world, with worldwide wind-generation capacity tripling in the five years leading up to 2004 [1]. Because wind turbines are large, flexible structures operating in noisy environments, they present a myriad of control problems that, if solved, could reduce the cost of wind energy. In contrast to constantspeed turbines (see “Wind Turbine Development and Types of Turbines”), variable-speed wind turbines are designed to follow wind-speed variations in low winds to maximize aerodynamic efficiency. Standard control laws [2] require that complex aerodynamic properties be well known so that the variable-speed turbine can maximize energy capture; in practice, uncertainties limit the efficient energy capture of a variable-speed turbine. The turbine used as a model for this article’s research is the Controls Advanced Research Turbine (CART) pictured in Figure 1. CART is located in Golden, Colorado, at the U.S. National Renewable Energy Laboratory’s National Wind Technology Center (see “The National Renewable Energy Laboratory and National Wind Technology Center”). A modern utility-scale wind turbine, as shown in Figure 2, has several levels of control systems. On the uppermost level, a supervisory controller monitors the turbine and wind resource to determine when the wind speed is sufficient to start up the turbine and when, due to high winds, the turbine must be shut down for safety. This type of control is the discrete if-then variety. On the middle level is turbine control, which includes generator torque control, blade pitch control, and yaw control. Generator torque control, performed using the power electronics, determines how much torque is extracted from the turbine, specifically, the high-speed shaft. The extracted torque opposes the aerodynamic torque provided by the wind and, thus, indirectly regulates the turbine speed. Depending on the pitch actuators and type of generator and power electronics, blade pitch control and generator torque control can operate quickly relative to the rotor-speed time constant. STANDARD AND ADAPTIVE TECHNIQUES FOR MAXIMIZING ENERGY CAPTURE
458 citations