Predefined-Time Barrier Function Super-Twisting SMC With Extended State Observer for Performance Enhancement of Grid-Connected PV-ESS Systems

Abstract

This paper introduces an advanced control framework aimed at enhancing the performance of a grid-connected Photovoltaic-Energy Storage Systems (PV-ESS). To effectively tackle the significant non-linearities and dynamic challenges arising from environmental intermittency and grid disturbances, an Extended State Observer (ESO)-based Predefined-Time Barrier Function Super Twisting Sliding Mode Control (PBF-STSMC) is proposed. The control scheme is systematically developed for the PV boost converter, the ESS bidirectional buck-boost converter, and the Voltage Source Inverter (VSI). Furthermore, the Python Snake Optimization Algorithm (PySOA) is designed to achieve high-efficiency Maximum Power Point Tracking (MPPT). Extensive modelling and simulation of the proposed framework are conducted using the MATLAB/Simulink environment. Additionally, an emphasis is placed on developing dynamic Low Voltage Ride Through (LVRT) logic that ensures compliance with the IEEE 1547 grid code during both asymmetrical and symmetrical faults. Moreover, a comprehensive statistical validation framework demonstrates the proposed controller’s superiority in transient stability, error minimization, and overall power quality compared to traditional SMC and Proportional-Integral (PI) controllers.