Version 1
: Received: 22 August 2024 / Approved: 22 August 2024 / Online: 22 August 2024 (11:28:54 CEST)
How to cite:
Muhsen, D. H.; Haider, H. T.; Al-Nidawi, Y. Parameter Identification of Photovoltaic Module based Triple-Diode using Hybrid Optimization Algorithms. Preprints2024, 2024081640. https://doi.org/10.20944/preprints202408.1640.v1
Muhsen, D. H.; Haider, H. T.; Al-Nidawi, Y. Parameter Identification of Photovoltaic Module based Triple-Diode using Hybrid Optimization Algorithms. Preprints 2024, 2024081640. https://doi.org/10.20944/preprints202408.1640.v1
Muhsen, D. H.; Haider, H. T.; Al-Nidawi, Y. Parameter Identification of Photovoltaic Module based Triple-Diode using Hybrid Optimization Algorithms. Preprints2024, 2024081640. https://doi.org/10.20944/preprints202408.1640.v1
APA Style
Muhsen, D. H., Haider, H. T., & Al-Nidawi, Y. (2024). Parameter Identification of Photovoltaic Module based Triple-Diode using Hybrid Optimization Algorithms. Preprints. https://doi.org/10.20944/preprints202408.1640.v1
Chicago/Turabian Style
Muhsen, D. H., Haider Tarish Haider and Yaarob Al-Nidawi. 2024 "Parameter Identification of Photovoltaic Module based Triple-Diode using Hybrid Optimization Algorithms" Preprints. https://doi.org/10.20944/preprints202408.1640.v1
Abstract
Identification the parameters of triple-diode electrical circuit structure of PV-module is a challenging issue and has been emphasized as an important research area. Accordingly, a hybrid evolutionary optimization algorithm is presented in this paper. Differential evolution algorithm (DEA) is hybridized with electromagnetism-like algorithm (EMA) in the mutation stage to enhance the reliability and efficiency of DEA. The presented algorithm is called differential evolution with integrated mutation per iteration (DEIMA). A new formula is presented to adapt the control parameters (mutation factor and crossover rate) of DEA and is based on a sigmoid function in terms of the current and previous fitness function values. Seven different experimental data sets are used to assess the performance of the proposed DEIMA. The results of the proposed PV modeling method are evaluated with other approaches in literature. According to different statistical criteria, DEIMA offered superiority in terms of root mean square error and main bias error by at least 5.4% and 10%, respectively, as compared to other methods. Furthermore, DEIMA needs 27.69 sec. as an average execution time less than other compared methods.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
