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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Main Authors: Muhsen D.H., Ghazali A.B., Khatib T., Abed I.A.
Other Authors: 56728928200
Format: Article
Published: Elsevier Ltd 2023
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spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description 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
author2 56728928200
author_facet 56728928200
Muhsen D.H.
Ghazali A.B.
Khatib T.
Abed I.A.
format Article
author Muhsen D.H.
Ghazali A.B.
Khatib T.
Abed I.A.
spellingShingle 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
author_sort 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
_version_ 1806427522206793728
score 13.222552