Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm
Algorithms; Evolutionary algorithms; Mean square error; Optimization; Parameter estimation; Photovoltaic cells; Coefficient of determination; DEAM; Five parameters; Hybrid differential evolution; Improved differential evolutions; IV characteristics; Photovoltaic modules; Root mean square errors; Ite...
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2023
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my.uniten.dspace-222782023-05-29T13:59:59Z Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm Muhsen D.H. Ghazali A.B. Khatib T. Abed I.A. 56728928200 56727852400 31767521400 55568292900 Algorithms; Evolutionary algorithms; Mean square error; Optimization; Parameter estimation; Photovoltaic cells; Coefficient of determination; DEAM; Five parameters; Hybrid differential evolution; Improved differential evolutions; IV characteristics; Photovoltaic modules; Root mean square errors; Iterative methods; algorithm; artificial intelligence; electromagnetic method; error analysis; experimental study; photovoltaic system In this paper, an improved differential evolution with adaptive mutation per iteration algorithm (DEAM) is proposed for extracting PV module's model parameters. DEAM utilizes the attraction-repulsion concept which is used in the electromagnetism to boost the mutation operation of the original differential evolution (DE). Furthermore, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. The proposed method has been validated by experimental data and other previous methods. The results of the proposed method show a high agreement between the experimental and simulated I- V characteristics. The average root mean square error, mean bias error, coefficient of determination and CPU-execution time of the proposed method are 1.744%, 0.158%, 99.21% and 18.5975. s respectively. According to the results, the proposed method offers better performance than other methods in terms of accuracy, CPU-execution time and convergence. � 2015 Elsevier Ltd. Final 2023-05-29T05:59:59Z 2023-05-29T05:59:59Z 2015 Article 10.1016/j.solener.2015.07.008 2-s2.0-84937500751 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937500751&doi=10.1016%2fj.solener.2015.07.008&partnerID=40&md5=9340345745cb84b1993ae142bda37687 https://irepository.uniten.edu.my/handle/123456789/22278 119 286 297 Elsevier Ltd Scopus |
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Algorithms; Evolutionary algorithms; Mean square error; Optimization; Parameter estimation; Photovoltaic cells; Coefficient of determination; DEAM; Five parameters; Hybrid differential evolution; Improved differential evolutions; IV characteristics; Photovoltaic modules; Root mean square errors; Iterative methods; algorithm; artificial intelligence; electromagnetic method; error analysis; experimental study; photovoltaic system |
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56728928200 |
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56728928200 Muhsen D.H. Ghazali A.B. Khatib T. Abed I.A. |
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Muhsen D.H. Ghazali A.B. Khatib T. Abed I.A. |
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Muhsen D.H. Ghazali A.B. Khatib T. Abed I.A. Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
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Muhsen D.H. |
title |
Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
title_short |
Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
title_full |
Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
title_fullStr |
Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
title_full_unstemmed |
Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
title_sort |
extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm |
publisher |
Elsevier Ltd |
publishDate |
2023 |
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1806427522206793728 |
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13.222552 |