Fault‐resilient control of parallel PV inverters using multi‐agent twin‐delayed deep deterministic policy gradient approach

Abstract

SummaryGrid‐tied photovoltaic system (GTPS) are widely favored due to their inherent benefits. The interface of parallel power electronic converters in these systems is rapidly increasing. The inverter in GTPS has a very important role in power conversion, transfer, and control. However, their challenging working conditions contribute to susceptibility to power switching device failures. Traditional fault diagnosis and tolerant approaches exhibit limitations in control efficiency, model dependency, and slow recovery from fault. This study proposes a multi‐agent twin delayed deep deterministic policy gradient (MATD3PG) configuration for intelligent parallel inverter control, fault diagnosis, and fault‐tolerant operation in GTPS structure. By leveraging the multi‐agent reinforcement learning (RL) framework, an optimal control of the parallel inverter can be achieved, encompassing fault‐tolerant operation using MATLAB Simulink/PLECS. The major advantage the given technique offers is that it carries out optimum fault‐tolerant operation without causing the system derating. Experimental findings demonstrate that the proposed fault‐tolerant model based on RL traditional methods is able to ensure continuous supply to the connected loads even during fault events. Further, the transition time from fault occurrence to recovery is found to be 6 ms, which is quite less compared with the fault‐tolerant techniques presented in literature. Through real‐time fault diagnosis‐based results, the proposed approach ensures precise tracking of reference currents, quicker response times, uninterrupted supply, and smooth transition to the post‐fault operation mode.