MPPT Study on Adaptive Chaos Particle Swarm Optimization Based on Local Shading
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DOI: 10.23977/csic.2018.0937
Author(s)
Jianhua Zhang, Zhanbiao Jia, And Menghui Xuan
Corresponding Author
Jianhua Zhang
ABSTRACT
Under complex shading conditions, the pv array outputs p-u image and it has multiple peaks, which may lead to the traditional maximum power point tracking algorithm, falling into the local extreme value and the tracking time being too long. For this reason, particle swarm optimization (PSO) is used to find the maximum power point under the shading condition. This paper is on the basis of the traditional particle swarm optimization algorithm, combining with chaos optimization, using linear synchronous learning factor change and adaptive inertia weight, and puts forward a new kind of self-adaptive chaotic particle swarm optimization algorithm (SA-CPSO). Compared with the PSO, this method can find the maximum power point of the system more quickly and accurately. The tracking time is only about 20% of the tracking time of PSO, moreover, the simulation model was built on Matlab/Simulink to verify its rapidity and accuracy.
KEYWORDS
Mppt Study, Local Shading, Particle Swarm Optimization