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

Dhiaa Halboot Muhsen, Abu Bakar Ghazali, Tamer Khatib and Issa Ahmed Abed

Renewable Energy, 2016, vol. 96, issue PA, 377-389

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.

Keywords: Differential evolution; Electromagnetism-like; Parameter extraction; Photovoltaic; Single-diode model (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:96:y:2016:i:pa:p:377-389

DOI: 10.1016/j.renene.2016.04.072

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