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A modified Adaptive Wavelet PID Control Based on Reinforcement Learning for Wind Energy Conversion System ControlSEDIGHIZADEH, M. , REZAZADEH, A.
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control, reinforcement, neural network, wavelet, wind energy
control(14), wind(10), adaptive(8), neural(7), networks(7), sedighizadeh(6), turbine(5), systems(5), energy(5), conversion(5)
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About this article
Date of Publication: 2010-05-31
Volume 10, Issue 2, Year 2010, On page(s): 153 - 159
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2010.02027
Web of Science Accession Number: 000280312600027
SCOPUS ID: 77954629341
Nonlinear characteristics of wind turbines and electric generators necessitate complicated and nonlinear control of grid connected Wind Energy Conversion Systems (WECS). This paper proposes a modified self-tuning PID control strategy, using reinforcement learning for WECS control. The controller employs Actor-Critic learning in order to tune PID parameters adaptively. These Actor-Critic learning is a special kind of reinforcement learning that uses a single wavelet neural network to approximate the policy function of the Actor and the value function of the Critic simultaneously. These controllers are used to control a typical WECS in noiseless and noisy condition and results are compared with an adaptive Radial Basis Function (RBF) PID control based on reinforcement learning and conventional PID control. Practical emulated results prove the capability and the robustness of the suggested controller versus the other PID controllers to control of the WECS. The ability of presented controller is tested by experimental setup.
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