Driving training‐based optimization (DTBO) for global maximum power point tracking for a photovoltaic system under partial shading condition

The presence of bypass diodes in photovoltaic (PV) arrays can mitigate the negative effects partial shading conditions (PSCs), which cause multiple peak characteristics at output. However, conventional maximum power point tracking (MPPT) methods develop errors and detect local (LMPP) instead global (GMPP) under certain circumstances. To address this issue, several artificial intelligence (AI)-based have been proposed, but they result complicated unreliable methodologies. This study introduces driving training-based optimization (DTBO) method, aims to (PS) problem quickly reliably (MPP) detection for PV systems. DTBO improves speed reduces fluctuations output during period. proposed method is extensively verified using Typhoon hardware-in-the-loop (HIL) 402 emulator compared such as particle swarm (PSO), JAYA, well recently adaptive JAYA (AJAYA) MPPT a system similar conditions.