A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models

Juan Zhao; Yujun Zhang; Shuijia Li; Yufei Wang; Yuxin Yan; Zhengming Gao; Juan Zhao; Yujun Zhang; Shuijia Li; Yufei Wang; Yuxin Yan; Zhengming Gao

doi:10.3934/mbe.2022264

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5638-5670. doi: 10.3934/mbe.2022264

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Research article

A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models

1.
School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China
2.
School of Computer Science, China University of Geosciences, Wuhan 430074, China
3.
Academy of arts, Jingchu University of Technology, Jingmen 448000, China
4.
School of computer engineering, Jingchu University of Technology, Jingmen 448000, China
5.
Institute of intelligent information technology, Hubei Jingmen industrial technology research institute, Jingmen 448000, China

Academic Editor: Ivan V. Izonin

Received: 12 February 2022 Revised: 17 March 2022 Accepted: 18 March 2022 Published: 31 March 2022

In order to have the highest efficiency in real-life photovoltaic power generation systems, how to model, optimize and control photovoltaic systems has become a challenge. The photovoltaic power generation systems are dominated by photovoltaic models, and its performance depends on its unknown parameters. However, the modeling equation of the photovoltaic model is nonlinear, leading to the difficulty in parameter extraction. To extract the parameters of the photovoltaic model more accurately and efficiently, a chaotic self-adaptive JAYA algorithm, called AHJAYA, was proposed, where various improvement strategies are introduced. First, self-adaptive coefficients are introduced to change the priority of information from the best search agent and the worst search agent. Second, by combining the linear population reduction strategy with the chaotic opposition-based learning strategy, the convergence speed of the algorithm is improved as well as avoid falling into local optimum. To verify the performance of the AHJAYA, four photovoltaic models are selected. The experimental results prove that the proposed AHJAYA has superior performance and strong competitiveness.
- photovoltaic model,
- parameter extraction,
- AHJAYA,
- self-adaptive,
- Linear population reduction,
- chaotic opposition-based learning
Citation: Juan Zhao, Yujun Zhang, Shuijia Li, Yufei Wang, Yuxin Yan, Zhengming Gao. A chaotic self-adaptive JAYA algorithm for parameter extraction of photovoltaic models[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5638-5670. doi: 10.3934/mbe.2022264

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Abstract

In order to have the highest efficiency in real-life photovoltaic power generation systems, how to model, optimize and control photovoltaic systems has become a challenge. The photovoltaic power generation systems are dominated by photovoltaic models, and its performance depends on its unknown parameters. However, the modeling equation of the photovoltaic model is nonlinear, leading to the difficulty in parameter extraction. To extract the parameters of the photovoltaic model more accurately and efficiently, a chaotic self-adaptive JAYA algorithm, called AHJAYA, was proposed, where various improvement strategies are introduced. First, self-adaptive coefficients are introduced to change the priority of information from the best search agent and the worst search agent. Second, by combining the linear population reduction strategy with the chaotic opposition-based learning strategy, the convergence speed of the algorithm is improved as well as avoid falling into local optimum. To verify the performance of the AHJAYA, four photovoltaic models are selected. The experimental results prove that the proposed AHJAYA has superior performance and strong competitiveness.

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