An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models
In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. I...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
AIMS Press
2022-03-01
|
| Series: | Mathematical Biosciences and Engineering |
| Subjects: | |
| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022263?viewType=HTML |
| _version_ | 1828406013081944064 |
|---|---|
| author | Yujun Zhang Yufei Wang Shuijia Li Fengjuan Yao Liuwei Tao Yuxin Yan Juan Zhao Zhengming Gao |
| author_facet | Yujun Zhang Yufei Wang Shuijia Li Fengjuan Yao Liuwei Tao Yuxin Yan Juan Zhao Zhengming Gao |
| author_sort | Yujun Zhang |
| collection | DOAJ |
| description | In order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid JAYA and Rao-1 algorithm, called EHRJAYA, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRJAYA has superior performance and rank in the leading position among the famous algorithms. |
| first_indexed | 2024-12-10T11:05:10Z |
| format | Article |
| id | doaj.art-acc4d07d8cad48638d3b23723f836aaa |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-10T11:05:10Z |
| publishDate | 2022-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-acc4d07d8cad48638d3b23723f836aaa2022-12-22T01:51:35ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-03-011965610563710.3934/mbe.2022263An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic modelsYujun Zhang0Yufei Wang1Shuijia Li2Fengjuan Yao3Liuwei Tao4Yuxin Yan5Juan Zhao6Zhengming Gao 71. School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China1. School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China2. School of Computer Science, China University of Geosciences, Wuhan 430074, China3. School of computer engineering, Jingchu University of Technology, Jingmen, 448000, China4. School of foreign languages, Jingchu University of Technology, Jingmen, 448000, China5. Academy of arts, Jingchu University of Technology, Jingmen 448000, China1. School of electronics and information engineering, Jingchu University of Technology, Jingmen 448000, China3. School of computer engineering, Jingchu University of Technology, Jingmen, 448000, China6. Institute of intelligent information technology, Hubei Jingmen industrial technology research institute, Jingmen 448000, ChinaIn order to maximize the acquisition of photovoltaic energy when applying photovoltaic systems, the efficiency of photovoltaic system depends on the accuracy of unknown parameters in photovoltaic models. Therefore, it becomes a challenge to extract the unknown parameters in the photovoltaic model. It is well known that the equations of photovoltaic models are nonlinear, and it is very difficult for traditional methods to accurately extract its unknown parameters such as analytical extraction method and key points method. Therefore, with the aim of extracting the parameters of the photovoltaic model more efficiently and accurately, an enhanced hybrid JAYA and Rao-1 algorithm, called EHRJAYA, is proposed in this paper. The evolution strategies of the two algorithms are initially mixed to improve the population diversity and an improved comprehensive learning strategy is proposed. Individuals with different fitness are given different selection probabilities, which are used to select different update formulas to avoid insufficient using of information from the best individual and overusing of information from the worst individual. Therefore, the information of different types of individuals is utilized to the greatest extent. In the improved update strategy, there are two different adaptive coefficient strategies to change the priority of information. Finally, the combination of the linear population reduction strategy and the dynamic lens opposition-based learning strategy, the convergence speed of the algorithm and ability to escape from local optimum can be improved. The results of various experiments prove that the proposed EHRJAYA has superior performance and rank in the leading position among the famous algorithms.https://www.aimspress.com/article/doi/10.3934/mbe.2022263?viewType=HTMLparameter extractionphotovoltaic modelsjaya algorithmrao-1 algorithmehrjaya algorithm |
| spellingShingle | Yujun Zhang Yufei Wang Shuijia Li Fengjuan Yao Liuwei Tao Yuxin Yan Juan Zhao Zhengming Gao An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models Mathematical Biosciences and Engineering parameter extraction photovoltaic models jaya algorithm rao-1 algorithm ehrjaya algorithm |
| title | An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models |
| title_full | An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models |
| title_fullStr | An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models |
| title_full_unstemmed | An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models |
| title_short | An enhanced adaptive comprehensive learning hybrid algorithm of Rao-1 and JAYA algorithm for parameter extraction of photovoltaic models |
| title_sort | enhanced adaptive comprehensive learning hybrid algorithm of rao 1 and jaya algorithm for parameter extraction of photovoltaic models |
| topic | parameter extraction photovoltaic models jaya algorithm rao-1 algorithm ehrjaya algorithm |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022263?viewType=HTML |
| work_keys_str_mv | AT yujunzhang anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT yufeiwang anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT shuijiali anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT fengjuanyao anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT liuweitao anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT yuxinyan anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT juanzhao anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT zhengminggao anenhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT yujunzhang enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT yufeiwang enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT shuijiali enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT fengjuanyao enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT liuweitao enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT yuxinyan enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT juanzhao enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels AT zhengminggao enhancedadaptivecomprehensivelearninghybridalgorithmofrao1andjayaalgorithmforparameterextractionofphotovoltaicmodels |