Advanced Intelligent Control Schemes for Enhancing Power Quality in DSTATCOM ‐Integrated PMSG ‐Based Wind Energy Systems With Battery Storage

The increasing penetration of nonlinear loads and power electronic devices in distribution networks has significantly intensified power quality (PQ) challenges, including harmonic distortion, voltage sags/swells, and reactive power imbalance. This paper proposes advanced intelligent control strategies for a Distributed Static Compensator (DSTATCOM) integrated with a Permanent Magnet Synchronous Generator (PMSG)‐based Wind Energy Conversion System (WECS) and Battery Storage System (BSS). The proposed schemes include (1) a hybrid fuzzy‐sliding mode control (HFSMC), (2) an artificial neural network‐based PI controller (ANN‐PI), and (3) a Butterfly Generator‐based particle swarm optimization PI controller (BG‐PSO‐PI) for Maximum Power Point Tracking (MPPT) and real‐time voltage/current compensation. Simulation results under nonlinear load and fault conditions demonstrate that the proposed BG‐PSO‐PI controller achieves a minimum source current Total Harmonic Distortion (THD) of 3.51%, outperforming ANN‐PI (3.57%), Fuzzy‐SMC (3.72%), and conventional PI (7.03%) controllers. Furthermore, the BG‐PSO‐PI controller maintains DC‐link voltage deviations within ±2%, compared to ±5% in traditional schemes, and achieves fault recovery in less than 0.05 s during grid disturbances. These results confirm that the proposed control architecture significantly improves PQ indices, enhances voltage stability, and ensures rapid dynamic response, making it suitable for modern grid‐integrated renewable energy systems.