Robust Control Strategies for a Three-Phase Grid-Connected PV System Based on Super-Twisting & Integral Sliding Mode Control
Abstract
This paper presents a robust control strategy for grid-connected photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Integral Sliding Mode Control (ISMC) to enhance performance. The proposed ap-proach addresses both Maximum Power Point Tracking (MPPT) and Voltage Source Inverter (VSI) control. An STA-based Artificial Neural Network (ANN) controller regulates the DC/DC Boost converter to optimize power ex-traction. The ANN generates a reference Maximum Power Point (MPP) volt-age, which is then compared with the measured voltage. The STA utilizes this error to generate the converter's Pulse Width Modulation (PWM) signal. On the AC side, dual STA-ISMC-based Voltage-Oriented Controllers (VOCs) generate control vectors for space vector modulation (SVM), significantly re-ducing root mean square error (RMSE) compared to conventional control methods and ensuring precise DC bus voltage regulation. The effectiveness of these control strategies has been rigorously evaluated using the MATLAB/Simulink environment under various operating conditions, including fluctuating PV output power and load profiles. The results demonstrate the su-periority of the proposed techniques in enhancing power quality and achieving optimal efficiency across a wide range of operating scenarios.