Reinforcement learning driven adaptive active frequency drift for fast and reliable islanding detection

Islanding detection remains a critical challenge in grid-connected photovoltaic (PV) systems, as failure to detect islanding conditions can compromise power quality, safety, and system stability. Existing active frequency drift (AFD) methods suffer from two major unresolved limitations: a large non-detection zone (NDZ) and fixed perturbation parameters that cannot adapt to changing grid and load conditions. Despite extensive studies on AFD, no existing method adaptively adjusts its perturbation parameters in response to grid dynamics, creating a significant research gap. To address this specific gap, this work introduces a novel AI-driven adaptive AFD method that uses reinforcement learning to dynamically…