Work

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

Year: 2025

Type: article

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.

Source: Energies

DOI: 10.3390/en18205461

Authors: Kai-Hung Lu, Chih-Ming Hong, Wen-Chang Tsai, Fu-Sheng Cheng

Institutions: Minnan University of Science and Technology, National Kaohsiung University of Science and Technology, Xiamen University, Xiamen University Tan Kah Kee College, Cheng Shiu University

Language: English

Topic: Wind Turbine Control Systems

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $3,137