Solar photovoltaic model parameter identification using robust niching chimp optimization

•A niching concept is inspired to enhance the original ChOA.•RN-ChOA proposes a novel constraint handling technique.•The RN-ChOA is applied to estimate parameters on the SM55, SW255, and KC200GT.•RN-ChOA is compared with ten well-known algorithms. Researchers are becoming increasingly interested in...

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Bibliographic Details
Published in:Solar energy Vol. 239; pp. 179 - 197
Main Authors: Bo, Qiuyu, Cheng, Wuqun, Khishe, Mohammad, Mohammadi, Mokhtar, Mohammed, Adil Hussein
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.06.2022
Pergamon Press Inc
Subjects:
Algorithms
Biogeography
Chimp optimization algorithm
Constraint modelling
Crystal structure
Crystallinity
Irradiance
Machine learning
Niching concept
Optimization algorithms
Parameter estimation
Parameter identification
Parameter robustness
Particle swarm optimization
Photovoltaic cells
Photovoltaic modules
Photovoltaics
RN-ChOA
Robustness
Solar cell
Solar energy
Solar cell
Niching concept
Chimp optimization algorithm
RN-ChOA
Photovoltaic modules
ISSN:0038-092X, 1471-1257
Online Access:Get full text
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Summary:•A niching concept is inspired to enhance the original ChOA.•RN-ChOA proposes a novel constraint handling technique.•The RN-ChOA is applied to estimate parameters on the SM55, SW255, and KC200GT.•RN-ChOA is compared with ten well-known algorithms. Researchers are becoming increasingly interested in studying how to accurately estimate the parameters of solar PV models. In this regard, this paper proposes a newly proposed nature-inspired technique named chimp optimization algorithm (ChOA) to create accurate and dependable PV models, such as single diode, double diodes, three diodes, and PV module models. In the PV models’ parameters estimation using optimization algorithms, two significant concerns need to be addressed: classifying various local/global optima and preserving these optimum values until the termination. Since ChOA is a general optimizer, it lacks an operator to address the two issues mentioned above. In order to address the mentioned problems, this paper embeds the niching technique in ChOA that includes the personal best qualities of PSO and a local search technique. In addition, a novel constraint handling approach is utilized to ensure that the algorithm is robust in tackling PV Models’ parameters estimation constraints. The outcome of RN-ChOA is evaluated using seven well-known optimization algorithms, including the whippy Harris hawks optimization algorithm (WHHOA), performance-guided JAYA (PGJAYA), enriched Harris hawks optimization algorithm (EHHOA), improved JAYA (IJAYA), birds mating optimizer (BMO), flexible particle swarm optimization algorithm (FPSO), chaotic biogeography-based optimizer (CBBO), and generalized oppositional teaching-learning algorithm (GOTLA), as well as dynamic Levy flight ChOA (DLF-ChOA) and weighted ChOA (WChOA) as the most recent modified version of ChOA. Furthermore, the performance of the RN-ChOA method has been assessed in a practical application for parameter evaluation of three widely-used commercial modules, namely, multi-crystalline (KC200GT), polycrystalline (SW255), and mono-crystalline (SM55), under a variety of temperature and irradiance conditions that cause changes in the photovoltaic model’s parameters. The findings demonstrate the robustness and excellent performance of the suggested approach.
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ISSN:0038-092X
1471-1257
DOI:10.1016/j.solener.2022.04.056