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

ABSTRACT This paper develops a hybrid adaptive control framework for improving low‐voltage ride‐through (LVRT) performance and transient stability in grid‐connected hybrid renewable energy systems. The proposed approach combines an adaptive least squares recursive Chebyshev fuzzy neural network (ALTSRCFNN) with super‐twisting sliding mode control (STSMC) to achieve robust voltage regulation under grid faults and parameter uncertainties. A Static Synchronous Compensator (STATCOM) is integrated to provide dynamic reactive power support during voltage sags. The ALTSRCFNN is employed to estimate system nonlinearities and update control gains online, while the STSMC ensures finite‐time convergence and mitigates chattering during severe disturbances. The effectiveness of the proposed controller is evaluated in MATLAB/Simulink under symmetrical and asymmetrical grid fault conditions and is benchmarked against conventional proportional–integral–derivative (PID) and feedforward neural network controllers. Simulation results demonstrate that the proposed method reduces voltage recovery time by approximately 25% and improves transient voltage stability by about 15% while satisfying LVRT grid code requirements. These findings confirm the suitability of the proposed control strategy for fault‐tolerant operation of large‐scale hybrid renewable power systems.