A Novel ALTSRCFNN-STSMC Approach for Enhancing Ride-Through in Hybrid Renewable Systems

This paper proposes an innovative Adaptive Least Squares Recursive Chebyshev Fuzzy Neural Network (ALTSRCFNN) combined with Super-Twisting Sliding-Mode Control (STSMC) for improving the low-voltage ride-through (LVRT) capability and stability of hybrid generation systems (HGS) under grid faults. The integration of Static Synchronous Compensators (STATCOM) enhances system robustness by mitigating oscillations and stabilizing bus voltage during transient conditions. Simulation results demonstrate that the proposed method significantly outperforms conventional PID controllers and other neural network-based methods in terms of accuracy, response speed, and voltage regulation. The ALTSRCFNN-STSMC effectively reduces voltage fluctuations, restores steady-state conditions within shorter timeframes, and ensures sufficient active and reactive power delivery under various environmental conditions. These findings indicate the proposed approach’s potential for practical applications in modern power systems, particularly in addressing stability challenges in renewable energy integration.