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This paper proposes a modified maximum power point tracking (MPPT) algorithm for photovoltaic systems under rapidly changing partial shading conditions (PSCs). The proposed algorithm integrates a genetic algorithm (GA) and the firefly algorithm (FA) and further improves its calculation process via a differential evolution (DE) algorithm. The conventional GA is not advisable for MPPT because of its complicated calculations and low accuracy under PSCs. In this study, we simplified the GA calculations with the integration of the DE mutation process and FA attractive process. Results from both the simulation and evaluation verify that the proposed algorithm provides rapid response time and high accuracy due to the simplified processing. For instance, evaluation results demonstrate that when compared to the conventional GA, the execution time and tracking accuracy of the proposed algorithm can be, respectively, improved around 69.4% and 4.16%. In addition, in comparison to FA, the tracking speed and tracking accuracy of the proposed algorithm can be improved around 42.9% and 1.85%, respectively. Consequently, the major improvement of the proposed method when evaluated against the conventional GA and FA is tracking speed. Moreover, this research provides a framework to integrate multiple nature-inspired algorithms for MPPT. Furthermore, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed equations, the advantages of which are rapid tracking speed with high accuracy under PSCs.

A rapid response optimization technique for photovoltaic maximum power point tracking (MPPT) under partial shading conditions (PSCs) is proposed in this study. To improve the solar MPPT tracking speed for rapidly-changing environmental conditions and to prevent the conventional firefly algorithm (FA) from becoming trapped at the local peaks and oscillations during the search process, a novel fusion algorithm, named the modified firefly algorithm (MFA), is proposed. The MFA integrates and modifies the processes of two algorithms, namely the firefly algorithm with neighborhood attraction (NaFA) and simplified firefly algorithm (SFA). A modified attraction process for the NaFA is used in the first iteration to avoid trapping at local maximum power points (LMPPs). In addition, in order to improve the convergence speed, the attractiveness factor of the attraction process is designed to be related to the power and position difference of the fireflies. Furthermore, the number of fireflies is designed to decrease in proportion with the iterations in the modified SFA process. Results from both the simulations and evaluations verify that the proposed algorithm offers rapid response with high accuracy and efficiency when encountering PSCs. In addition, the MFA can avoid becoming trapped at LMPPs and ease the oscillations during the search process. Consequently, the proposed method could be considered to be one of the most promising substitutes for existing approaches. In addition, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed parameters.

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