Levy Flight‐Enhanced Coot Optimization Algorithm‐Based MPPT for PV Systems Under Partial Shading Conditions

ABSTRACT The power generated by Photovoltaic (PV) systems is influenced by multiple factors, with irradiance and temperature being the most significant. Under Partial Shading Conditions (PSC), uneven irradiance distribution across PV arrays leads to a substantial reduction in output power. Furthermore, the P‐V characteristics of PV systems under such conditions exhibit multiple peaks, with the number of peaks increasing proportionally to the number of PV modules. Conventional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O), Hill Climbing (HC), and Incremental Conductance (INC), struggle to locate the global maximum power point (GMPP) on the P‐V curve in these scenarios. To address the limitations of the Coot Optimization Algorithm (COA)—specifically its slow tracking speed and significant oscillations under PSC, this paper proposes a Levy Flight‐enhanced Coot Optimization Algorithm (LF‐COA) for global MPPT of PV systems under shading conditions. Static and dynamic irradiance simulation experiments conducted in MATLAB/SIMULINK demonstrate that LF‐COA outperforming the Modified Firefly Algorithm (MFA), Improved Particle Swarm Optimization (IPSO) algorithm, and the conventional COA in performance metrics.