An efficient ANN-based global maximum power point searching approach for solar PV system under partial shading conditions

Lakshminarayana Munirathnam (M.S. Ramaiah Institute of Technology)
Yogeesh Amabalagere Chandrashekaraiah (Government Engineering College (Affiliated to Visvesvaraya Technological University))
Somu Pundalik Parande (Basaveshwar Engineering College)
Vani Bengaluru Purushothama (Visvesvaraya Technological University)
Ramya Mullur Varadhanayaka (JSS Academy of Technical Education)
Bellary Kursheed (Don Bosco Institute of Technology)

Bulletin of Electrical Engineering and Informatics
Vol 15, No 5: October 2026

For solar photovoltaic (PV) systems operating in partial shading conditions (PSCs), this research suggests a simplified maximum power point tracking (MPPT) controller based on artificial neural networks (ANNs). By adding multiple local peaks (LPs) to the power-voltage (P-V) curve, PSCs make it impossible for traditional techniques to follow the global maximum power point (GMPP), which results in substantial energy loss and decreased system reliability. In order to overcome this, a DC–DC boost converter's (BC's) duty cycle is adaptively controlled by the suggested ANN-based controller for precise GMPP tracking. Three shading scenarios are used to validate the approach on a 3×2 PV array. According to simulation results, tracking efficiency is higher than 99.4% and overall system efficiency is higher than 99.3%. The ANN greatly reduces oscillations around the GMPP by achieving quick convergence (