Deep Reinforcement Learning-Assisted Universal Active Filter for Power Quality Enhancement in a SyRG-Based Standalone Renewable Energy System with Hybrid Energy Storage

Abstract

The rapid deployment of standalone renewable energy systems has increased the need for intelligent power quality enhancement techniques capable of operating under highly dynamic and nonlinear conditions. Conventional proportionalintegral (PI) controllers and recently developed Artificial Neural Network (ANN)-based controllers improve voltage regulation and harmonic mitigation; however, their performance depends on prior training and fixed learning structures. This paper proposes a Deep Reinforcement Learning (DRL)-assisted Universal Active Filter (UAF) for a Synchronous Reluctance Generator (SyRG)-based standalone renewable energy system integrated with a photovoltaic array and hybrid battery energy storage system. Unlike conventional controllers, the proposed DRL controller continuously learns the optimal switching policy from real-time operating conditions without requiring repeated controller tuning. The controller coordinates the operation of the series and shunt voltage source converters to suppress harmonic currents, compensate reactive power, stabilize the DC-link voltage, and maintain sinusoidal load voltage under varying renewable generation and nonlinear load conditions. MATLAB/Simulink simulations demonstrate significant improvements in dynamic response, voltage regulation, power factor correction, and harmonic suppression compared with conventional PI with ANN controller. The proposed intelligent controller effectively minimizes Total Harmonic Distortion (THD), improves converter efficiency, enhances system robustness against disturbances, and satisfies IEEE-519 power quality standards, making it suitable for next-generation standalone renewable microgrids.