Adaptive Maximum Power Capture Control for Wind Power Systems with VRB Storage Using SVR-Based Sensorless Estimation and FPNN-IPSO Optimization

Kai-Hung Lu, Chih-Ming Hong () and Fu-Sheng Cheng
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Kai-Hung Lu: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Chih-Ming Hong: Department of Telecommunication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan
Fu-Sheng Cheng: Department of Electrical Engineering, Cheng-Shiu University, Kaohsiung 833301, Taiwan

Energies, 2025, vol. 18, issue 20, 1-22

Abstract: This study proposes a novel sensorless maximum power capture control strategy for variable-speed wind energy conversion systems employing a permanent magnet synchronous generator (PMSG). The proposed method integrates a fuzzy probabilistic neural network (FPNN) with an improved particle swarm optimization (IPSO) algorithm to enable adaptive learning capabilities. Additionally, support vector regression (SVR) is employed to estimate wind speed without the use of mechanical sensors, thereby enhancing system reliability and reducing maintenance requirements. A vanadium redox battery (VRB) is integrated to enhance power stability under fluctuating wind conditions. Simulation results demonstrate that the proposed FPNN-IPSO-based controller achieves superior performance compared to conventional Takagi–Sugeno–Kang (TSK) fuzzy and proportional–integral (PI) controllers. Specifically, the FPNN-IPSO controller exhibits notable improvements in average power output, tracking accuracy, and overall system efficiency. The proposed method increases power output by 9.71% over the PI controller and supports Plug-and-Play operation, making it suitable for intelligent microgrid integration. This work demonstrates an effective approach for intelligent, sensorless MPC control in hybrid wind–battery microgrids.

Keywords: fuzzy probabilistic neural network (FPNN); improved particle swarm optimization (IPSO); support vector regression (SVR); vanadium redox battery (VRB); maximum power capture; sensorless control; microgrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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