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Abstract
In this study, the output characteristics of partial modules in a photovoltaic module array when subject to shading were first explored. Then, an improved particle swarm optimization (PSO) algorithm was applied to track the global maximum power point (MPP), with a multi-peak characteristic curve. The improved particle swarm optimization algorithm proposed, combined with the artificial bee colony (ABC) algorithm, was used to adjust the weighting, cognition learning factor, and social learning factor, and change the number of iterations to enhance the tracking performance of the MPP tracker. Finally, MATLAB software was used to carry out a simulation and prove the improved that the PSO algorithm successfully tracked the MPP in the photovoltaic array output curve with multiple peaks. Its tracking performance is far superior to the existing PSO algorithm.
Highlights
Since the output power of the photovoltaic module array is influenced by external factors such as the current amount of sunlight, ambient temperature, and stains, the output power presents a non-linear change
PSOand of iterations track number the actual power whilemaximum the particle swarm optimization (PSO) proposed in Reference proposed in Reference [21] and the PSO proposed in this study can quickly track the actual maximum power point (MPP)
Had an unideal average number of iterations in multiple simulations of Case 3, mainly because the multi-peak values had a highly approximate local solution, resulting in the traditional PSO and the PSO proposed in Reference [21] failing to skip the local solution; a higher number of iterations were needed to track the actual MPP
Summary
Since the output power of the photovoltaic module array is influenced by external factors such as the current amount of sunlight, ambient temperature, and stains, the output power presents a non-linear change. The constant voltage tracking method [2] uses the MPP of the characteristic curve of respective output powers and voltages, which generally corresponds to a constant voltage and achieves maximum power tracking It has the advantage of simple control without complicated calculations, but its disadvantage is that, when the atmospheric environment substantially changes, the new MPP cannot be tracked. This paper uses the ABC optimization method to adjust the parameters of the traditional PSO In this way, the number of iterations can be reduced and the time of the maximum power point tracking can be shortened. The combined method of PSO and ABC can reduce the standard deviations of the needed number of iterations and even exceed the deterministic techniques
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