Version 1
: Received: 4 July 2023 / Approved: 5 July 2023 / Online: 5 July 2023 (07:20:09 CEST)
Version 2
: Received: 5 July 2023 / Approved: 6 July 2023 / Online: 6 July 2023 (09:31:21 CEST)
How to cite:
Memon, Z.A.; Akbari, M.A.; Zare, M. An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell/Module Models. Preprints2023, 2023070270. https://doi.org/10.20944/preprints202307.0270.v2
Memon, Z.A.; Akbari, M.A.; Zare, M. An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell/Module Models. Preprints 2023, 2023070270. https://doi.org/10.20944/preprints202307.0270.v2
Memon, Z.A.; Akbari, M.A.; Zare, M. An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell/Module Models. Preprints2023, 2023070270. https://doi.org/10.20944/preprints202307.0270.v2
APA Style
Memon, Z.A., Akbari, M.A., & Zare, M. (2023). An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell/Module Models. Preprints. https://doi.org/10.20944/preprints202307.0270.v2
Chicago/Turabian Style
Memon, Z.A., Mohammad Amin Akbari and Mohsen Zare. 2023 "An Improved Cheetah Optimizer for Accurate and Reliable Estimation of Unknown Parameters in Photovoltaic Cell/Module Models" Preprints. https://doi.org/10.20944/preprints202307.0270.v2
Abstract
Solar photovoltaic systems are becoming increasingly popular due to their outstanding environmental, economic, and technical characteristics. To simulate, manage, and control photovoltaic (PV) systems, the primary challenge is identifying unknown parameters accurately and reliably as early as possible using a robust optimization algorithm. This paper proposes a newly developed cheetah optimizer (CO) and improved CO (ICO) to extract parameters from various PV models. This algorithm, inspired by cheetah hunting behavior, includes several basic strategies: searching, sitting, waiting, and attacking. Although this algorithm has shown remarkable capabilities in solving large-scale problems, it needs improvement concerning its convergence speed and computing time. Here, an improved CO (ICO) is presented to identify solar power model parameters for this purpose. Single-, double-, and PV module models are investigated to test ICO's parameter estimation performance. Statistical analysis uses minimum, mean, maximum, and standard deviation. Furthermore, to improve confidence in test results, Wilcoxon and Freidman rank nonparametric tests are also performed. Compared to other state-of-the-art optimization algorithms, the ICO algorithm is proven to be highly reliable and accurate when identifying PV parameters.
Keywords
Solar energy; metaheuristics; optimization; energy management; solar cell; photovoltaic modeling
Subject
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.
Commenter: Zulfiqar Memon
Commenter's Conflict of Interests: Author