Super-twisting MPPT enhanced via grey wolf optimization for dynamic PV operation

Abstract

This paper introduces a hybrid maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems under rapidly varying irradiance conditions. The approach combines the super-twisting algorithm (STA), a second-order sliding mode control technique, with the grey wolf optimizer (GWO) in a coordinated framework where control action and parameter adaptation are jointly addressed. Unlike conventional MPPT methods that treat control and optimization separately, the proposed scheme improves transient response while limiting steady-state oscillations. The method is evaluated through MATLAB/Simulink simulations under multiple dynamic irradiance profiles, including fast-changing environmental conditions. Performance is assessed using complementary metrics, namely tracking efficiency, convergence dynamics, and root mean square error (RMSE), to provide an objective analysis. Results show that the STA-GWO strategy achieves faster convergence and improved stability compared to conventional SMC-GWO. It reaches an average tracking efficiency of 99.34%, compared to 99.19% for SMC-GWO, with reduced power fluctuations reflected by a lower RMSE. These improvements indicate a better trade-off between dynamic performance and steady-state accuracy. While this study is based on simulations, its findings require experimental validation. Future work will therefore include real-time implementation to confirm the practical applicability of the proposed approach.