Genetic Algorithm-Optimized Active Disturbance Rejection Control for Robust Voltage Regulation in Standalone DFIG-Based Wind Energy Systems with Battery Storage: Real-Time Experimental Implementation

Abstract

The design, modeling, and experimental validation of a Genetic Algorithm (GA)-optimized Active Disturbance Rejection Control (ADRC) strategy for voltage regulation in a standalone Wind Energy Conversion System (WECS) based on a Doubly Fed Induction Generator (DFIG) are presented in this study. The proposed system is designed to operate under variable wind conditions and nonlinear load profiles, with a Battery Energy Storage System (BESS) connected to the rotor side to enhance system performance and stability. A battery management algorithm is incorporated to ensure safe operation and maintain the battery state of charge (SOC) within predefined limits. The proposed cascaded control strategy combines a PI controller for the outer stator-voltage loop with an ADRC controller for the inner rotor-current loop, while GA optimization is employed to determine the controller parameters. The control strategy is evaluated through detailed simulations in MATLAB/Simulink and experimentally validated using a real-time dSPACE DS1104 platform. The results show that GA optimization improves voltage regulation, transient response, and disturbance rejection compared with the non-optimized ADRC-based control strategy. Moreover, the integration of the battery management algorithm ensures reliable power exchange and effective SOC regulation, demonstrating the practical feasibility of the proposed approach for standalone renewable energy applications.