Publication: Extraction of photovoltaic module model's parameters using an improved hybrid differential evolution/electromagnetism-like algorithm
Date
2015
Authors
Muhsen D.H.
Ghazali A.B.
Khatib T.
Abed I.A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
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.
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