A Comprehensive Review and Application of Metaheuristics in Solving the Optimal Parameter Identification Problems

Hegazy Rezk (), A. G. Olabi, Tabbi Wilberforce and Enas Taha Sayed
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Hegazy Rezk: Department of Electrical Engineering, College of Engineering in Wadi Alddawasir, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
A. G. Olabi: Sustainable Energy & Power Systems Research Centre, RISE, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
Tabbi Wilberforce: Mechanical Engineering and Design, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK
Enas Taha Sayed: Department of Chemical Engineering, Faculty of Engineering, Minia University, Elminia 27272, Egypt

Sustainability, 2023, vol. 15, issue 7, 1-24

Abstract: For many electrical systems, such as renewable energy sources, their internal parameters are exposed to degradation due to the operating conditions. Since the model’s accuracy is required for establishing proper control and management plans, identifying their parameters is a critical and prominent task. Various techniques have been developed to identify these parameters. However, metaheuristic algorithms have received much attention for their use in tackling a wide range of optimization issues relating to parameter extraction. This work provides an exhaustive literature review on solving parameter extraction utilizing recently developed metaheuristic algorithms. This paper includes newly published articles in each studied context and its discussion. It aims to approve the applicability of these algorithms and make understanding their deployment easier. However, there are not any exact optimization algorithms that can offer a satisfactory performance to all optimization issues, especially for problems that have large search space dimensions. As a result, metaheuristic algorithms capable of searching very large spaces of possible solutions have been thoroughly investigated in the literature review. Furthermore, depending on their behavior, metaheuristic algorithms have been divided into four types. These types and their details are included in this paper. Then, the basics of the identification process are presented and discussed. Fuel cells, electrochemical batteries, and photovoltaic panel parameters identification are investigated and analyzed.

Keywords: metaheuristic optimization; parameters identification; photovoltaic; battery storage; fuel cells (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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