Improving maximum power point tracking efficiency in solar photovoltaic systems using super-twisting algorithm and grey wolf optimizer

Abstract

This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Grey Wolf Optimizer (GWO). The STA-GWO-MPPT method improves efficiency in dynamic conditions by using STA for control and GWO for parameter optimization, enhancing stability and robustness. Performance evaluation is conducted through MATLAB/Simulink simulations and experimental validation on a small-scale test bench. Various quantitative metrics, including rise time, settling time, power production, efficiency, root mean square error (RMSE), and standard deviation (STD), are employed for assessment. Results indicate significantly faster convergence speeds for the proposed method compared to conventional MPPT techniques. Specifically, the rise time for the proposed method is 0.0129 seconds, outperforming Fuzzy Logic Control (FLC) (0.2638 seconds) and Grey Wolf Optimizer with Sliding Mode Control (GWO-SMC) (0.0181 seconds). Additionally, the proposed method exhibits superior tracking efficiency, with an average efficiency of 99.33%, surpassing FLC (96.93%) and GWO-SMC (99.19%). Moreover, it reduces power fluctuations, with an RMSE of 7.819% and STD of 6.547%, compared to FLC (RMSE: 13.471%, STD: 4.519%) and GWO-SMC (RMSE: 8.507%, STD: 6.108%). Overall, this study contributes valuable insights into enhancing MPPT efficiency in solar PV systems, with implications for both research and practical applications.