Parameter extraction of photovoltaic models using a memory-based improved gorilla troops optimizer. (15th January 2022)

• Record Type: Journal Article
• Authors: Abdel-Basset, Mohamed; El-Shahat, Doaa; Sallam, Karam M.; Munasinghe, Kumudu
• Is Part Of: Energy conversion and management. Volume 252(2022)
• Publication Date: 2022-01-15
• Subjects: Parameter identification -- Photovoltaic models -- Solar cell -- Gorilla troops optimizer; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2021.115134
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A Memory-based Improved Gorilla Troops Optimizer (MIGTO) is proposed. Two improvements are introduced to the proposed MIGTO algorithm. MIGTO is used to determine unknown parameters of photovoltaic model. Four different PV models are evaluated using one, two, three diode and PV models. Comprehensive experimental results were conducted to test the efficiency of MIGTO. Abstract: The parameter extraction of the PV model is a challenging issue owing to its multi-model and nonlinear characteristics. Moreover, these characteristics of the problem render the algorithms tackling it susceptible to being stuck in local optima. Nevertheless, it is imperative to accurately estimate the parameters due to their significant impact on the efficiency of the Photovoltaic (PV) system in terms of current and power generation. Hence, this paper proposes a new metaheuristic algorithm called the Memory-based Improved Gorilla Troops Optimizer (MIGTO). Furthermore, the Explorative Gorilla with an Adaptive Mutation Mechanism (EGAMM) and the Gorilla memory-saving technique are two major improvements that support the MIGTO superiority. EGAMM primarily comprises two main operators (explorative gorilla operator, adaptive mutation operator). The explorative gorilla operator enhances the search capability to explore new gorillas' positions in the search space to escape from local optima with a small probability of 0.1. Otherwise, MIGTO updates the new gorilla position using the adaptive mutation operator by selecting two random gorillas. Additionally, the GMS technique promotes the quality of the gorillas by keeping a history of the previous positions to be compared to the current ones. Several experiments are conducted between MIGTO and other well-known metaheuristic algorithms. The experimental results and statistical analyses prove the outperformance of MIGTO in effectively extracting the parameters of different PV models, including the single diode model, the double diode model, the triple diode model, and the photovoltaic module model. Additionally, the performance of the algorithms is tested using the manufacturer's datasheet.