Xuemei Yang, Wenqing Zhang, and Wenwen Zou
Photovoltaic power system, maximum power tracking, fuzzy logic, quantum swarm algorithm, conductance increment method
This paper suggests a novel photovoltaic (PV) power tracking model to address the limitations of maximum power point tracking (MPPT) to fully utilise renewable resources like solar energy. The model is based on the quantum swarm algorithm (QSA), which is enhanced by the fuzzy logic (FL) algorithm to speed up optimisation and ensure optimisation accuracy, along with the conductivity incremental method, which is enhanced by the variable step-size algorithm to guarantee convergence of the algorithm. The purpose of simulation experiments is to validate the model’s viability. According to the experimental findings, the upgraded genetic gene algorithm discovered the maximum electric power point at 2,034 W, while the improved quantum swarm method found the greatest electric power point at 2,920 W. The tracking speed was increased by 21.95% compared to the simulation results (SR) of the original conductivity incremental technique, which were 0.41 s and 0.32 s, respectively. The light intensity was increased from 200 W/m2 to 400 W/m2. The SR are 0.46 s and 0.38 s, respectively, and the tracking speed is enhanced by 17.39%. The average output power of the PV arrays is 41.52 W and 39.86 W, respectively. The tracking rate can be increased by 15% to 25% using the enhanced conductivity increment approach. It is clear that this study method has enhanced and ensured the tracking speed and accuracy of the maximum power of PV power generation, which has the practical value of energy conservation and maximising the use of solar energy resources.
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