Improved Photovoltaic MPPT Algorithm Based on Ant Colony Optimization and Fuzzy Logic Under Conditions of Partial Shading

Under conditions of partial shadowing, traditional Maximum Power Point Tracking (MPPT) algorithms face difficulties in precisely locating the maximum power point (MPP) of the system. To address this problem, this paper proposes an optimization algorithm, Ant-Fuzzy Optimization (AFO) algorithm. AFO utilizes the global search capability of the ant colony optimization (ACO) algorithm and the high precision performance of the fuzzy logic (FL) algorithm, mitigating the tendency of the fuzzy algorithm to fall into local optima in shadow conditions. Internally, the AFO algorithm comprises two parallel logics, selecting different strategies for tracking based on varying environmental states, achieving a balance between tracking accuracy and computational efficiency. This intelligent logic selection mechanism allows the algorithm to flexibly adapt to diverse working environments of photovoltaic (PV) arrays, enhancing the robustness and adaptability of the system. The paper establishes corresponding simulation models in MATLAB/SIMULINK and validates AFO through hardware experiments on the dSPACE real-time simulation system. The results demonstrate the feasibility and effectiveness of AFO in practical environments. Both simulation and experimental prototypes indicate that AFO can rapidly and accurately extract the maximum power point with an accuracy of 98.7%. Furthermore, AFO exhibits rapid dynamic response characteristics, reaching steady state within 0.9 seconds, providing a reliable solution for optimizing the output power of photovoltaic arrays.