Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module

Abhishek Sharma; Abhinav Sharma; Moshe Averbukh; Vibhu Jately; Shailendra Rajput; Brian Azzopardi; Wei Hong Lim

doi:10.1038/s41598-023-37824-4

Performance investigation of state-of-the-art metaheuristic techniques for parameter extraction of solar cells/module

Sci Rep. 2023 Jul 10;13(1):11134. doi: 10.1038/s41598-023-37824-4.

Authors

Abhishek Sharma¹, Abhinav Sharma², Moshe Averbukh³, Vibhu Jately², Shailendra Rajput⁴, Brian Azzopardi^{5

6}, Wei Hong Lim⁷

Affiliations

¹ Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, 248002, India.
² Department of Electrical and Electronics Engineering, University of Petroleum and Energy Studies, Dehradun, 248007, India.
³ Department of Electrical and Electronics Engineering, Ariel University, 40700, Ariel, Israel.
⁴ College of Engineering, Xi'an International University, Xi'an, 710077, China.
⁵ MCAST Energy Research Group (MCAST Energy), Institute of Engineering and Transport, Malta College of Arts, Science and Technology (MCAST), Triq Kordin, Paola, PLA 9032, Malta.
⁶ The Foundation for Innovation and Research - Malta, 65 Design Centre Level 2, Tower Road, BKR 4012, Birkirkara, Malta.
⁷ Faculty of Engineering, Technology and Built Environment, UCSI University, 56000, Cheras, Kuala Lumpur, Malaysia. limwh@ucsiuniversity.edu.my.

Abstract

One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell's precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E-03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E-03 and 4.7571E-05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.