Lei Wang, Jinzhao Cui, Chao Ge, Zhixin Lun
Photovoltaic power generation systems exhibit multi-peak power-voltage characteristics under partial shading conditions, severely limiting the effectiveness of conventional maximum power point tracking methods. This paper proposes a two-layer hierarchical MPPT architecture based on an adaptive fuzzy-weighted eagle perching optimization algorithm, designated AF-EPO. Unlike existing fuzzy-metaheuristic MPPT hybrids that apply fuzzy logic as an exogenous regulator to secondary control variables while retaining fixed-parameter core dynamics, AF-EPO embeds the fuzzy inference system directly into the endogenous EPO scaling factor, jointly driven by iteration progress and population diversity, thereby addressing three persistent limitations of metaheuristic MPPT through structural changes absent from existing fuzzy-hybrid formulations: the fixed-parameter exploration-exploitation dilemma, the computational overhead of unconditional global search, and the residual steady-state power oscillation that persists in all population-based methods. A slope sign-reversal detection module first identifies whether the power-voltage curve is unimodal or multi-modal, activating the global search layer only when partial shading is confirmed. In the global layer, a 25-rule Mamdani fuzzy inference system dynamically adjusts the EPO scaling factor according to iteration progress and population diversity, balancing exploration and exploitation throughout the search. A variable-step incremental conductance controller then refines the operating point to suppress steady-state oscillation. Simulations across three scenarios demonstrate that AF-EPO reduces tracking time by 47.3%, power oscillation rate by 74.6%, and energy loss by 38.2% compared with standard EPO. Hardware experiments on a TMS320F28335 DSP platform confirm tracking efficiencies exceeding 98.1%, with a maximum simulation-to-experiment deviation of 1.1%, validating the practical effectiveness of the proposed method.