A comparative study of evolutionary algorithms and adapting control parameters for estimating the parameters of a single-diode photovoltaic module's model. (October 2016)

• Record Type: Journal Article
• Authors: Muhsen, Dhiaa Halboot; Ghazali, Abu Bakar; Khatib, Tamer; Abed, Issa Ahmed
• Is Part Of: Renewable energy. Volume 96:Part A(2016)
• Page Start: 377
• Page End: 389
• Publication Date: 2016-10
• Subjects: Differential evolution -- Electromagnetism-like -- Parameter extraction -- Photovoltaic -- Single-diode model; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2016.04.072
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: This paper proposes different evolutionary algorithms, such as differential evolution and electromagnetism-like algorithms, to extract the five parameters of a single-diode photovoltaic module's model. Hybrid evolutionary algorithms are proposed with integrated and adaptive mutation per iteration schemes. In addition, a new formula to adjust the mutation scaling factor and crossover rate for each generation is proposed. Analyses are performed based on experimental data points under different weather conditions to explain the robustness and reliability of the proposed methods. Results show that the proposed hybrid algorithms, namely, evolutionary algorithm with integrated mutation per iteration and evolutionary algorithm with adaptive mutation per iteration, exhibit better performance than electromagnetism-like algorithm and other methods in terms of accuracy, CPU execution time, and convergence. The proposed hybrid algorithms offer a root mean square error, mean bias error, coefficient of determination and CPU execution time around 0.062, 0.006 and 0.992, and less than 20 s respectively. Furthermore, the feasibility of the proposed methods is validated by comparing the obtained results with those of other methods under various statistical errors. As a conclusion, the proposed hybrid algorithms offer root mean square error and mean bias error less than other methods by 14% at least. Highlights: Hybrid DE/EM algorithms are proposed to extract five parameters of PV module. Control parameters of hybrid algorithms are adjusted automatically. The proposed methods offer better performance than other in literature.