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

Kai-Hung Lu,

Chih-Ming Hong

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Abstract 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 Static Synchronous Compensators (STATCOM) enhances system robustness by mitigating oscillations stabilizing bus voltage during transient conditions. Simulation results demonstrate that proposed method significantly outperforms conventional PID controllers other neural network-based methods in terms accuracy, response speed, regulation. ALTSRCFNN-STSMC effectively reduces fluctuations, restores steady-state conditions within shorter timeframes, ensures sufficient active reactive power delivery various environmental These findings indicate approach’s potential practical applications modern systems, particularly addressing challenges renewable energy integration.

Language: Английский

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

Kai-Hung Lu,

Chih-Ming Hong

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Abstract 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 Static Synchronous Compensators (STATCOM) enhances system robustness by mitigating oscillations stabilizing bus voltage during transient conditions. Simulation results demonstrate that proposed method significantly outperforms conventional PID controllers other neural network-based methods in terms accuracy, response speed, regulation. ALTSRCFNN-STSMC effectively reduces fluctuations, restores steady-state conditions within shorter timeframes, ensures sufficient active reactive power delivery various environmental These findings indicate approach’s potential practical applications modern systems, particularly addressing challenges renewable energy integration.

Language: Английский

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