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Neural network compensation control for output power optimization of wind energy conversion system based on data-driven control. (English) Zbl 1248.93153

Summary: Due to the uncertainty of wind and because Wind Energy Conversion Systems (WECSs) have strong nonlinear characteristics, an accurate modeling of the WECS is difficult. To solve this problem, a data-driven control technology is proposed and data-driven controllers for the WECS are designed basing on the Markov model. Neural networks are designed to optimize the output of the system based on the data-driven control system model. In order to improve the efficiency of the neural network training, three different learning rules are compared. Analysis results and SCADA data of the wind farm are compared, and it is shown that the method effectively reduces fluctuations of the generator speed, the safety of the wind turbines can be enhanced, the accuracy of the WECS output is improved, and more wind energy is captured.

MSC:

93E03 Stochastic systems in control theory (general)
93C41 Control/observation systems with incomplete information
93C10 Nonlinear systems in control theory
92B20 Neural networks for/in biological studies, artificial life and related topics
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