Title: A novel multi-verse optimiser with integrated guidance strategy for parameters identification of photovoltaic models

Authors: Jinkun Luo; Fazhi He; Xiaoxin Gao

Addresses: School of Computer Science, Wuhan University, Wuhan, 430072, China ' School of Computer Science, Wuhan University, Wuhan, 430072, China ' School of Computer Science, Wuhan University, Wuhan, 430072, China

Abstract: To improve the efficiency of converting solar energy into electricity with photovoltaic (PV) system, it is essential to obtain the satisfactory PV system parameters. However, the process of identifying PV parameters easily falls into the local optimum with the unreliability of traditional parameter extraction techniques, due to the multi-modal characteristics of the equivalent circuit equation of a PV model. So accurately and reliably identifying PV model parameters is still a challenging and popular topic. In this study, a novel multi-verse optimiser with integrated guidance strategy (MVOIGS) is designed to identify satisfactory photovoltaic parameters. First, multi-level guidance mechanism (MGM) is designed to replace the search mechanism of original multi-verse optimiser (MVO) to improve the accuracy of solution by enhancing global and local search ability. Second, while MVO improved by MGM can enhance accuracy of solution, it may also face the problem of unreliability because it is a stochastic optimisation algorithm. Thus, we propose a weighted mutation disturbance method (WMDM) to reduce the probability of unreliability as far as possible by disturbing the selected individuals of population generated by MGM. Finally, the proposed MVOIGS is applied to solve parameters identification. Overall, experimental results demonstrate that the proposed MVOIGS outperforms 17 optimisation algorithms.

Keywords: multi-verse optimiser; MVO; solar energy; photovoltaic models; parameters identification; meta-heuristic algorithm.

DOI: 10.1504/IJBIC.2022.121238

International Journal of Bio-Inspired Computation, 2022 Vol.19 No.2, pp.124 - 133

Received: 18 Jul 2021
Accepted: 11 Nov 2021
Published online: 01 Mar 2022 *

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