Abstract
The use of metaheuristics in estimating the exact parameters of solar cell systems contributes greatly to performance improvement. The nonlinear electrical model of the solar cell has some parameters whose values are necessary to design photovoltaic (PV) systems accurately. The metaheuristic algorithms used to determine solar cell parameters have achieved remarkable success; however, most of these algorithms still produce local optimum solutions. In any case, changing to more suitable candidates through elephant herd optimization (EHO) equations is not guaranteed; in addition, instead of making parameter α adaptive throughout the evolution of the EHO, making them adaptive during the evolution of the EHO might be a preferable choice. The EHO technique is used in this work to estimate the optimum values of unknown parameters in single-, double-, and three-diode solar cell models. Models for five, seven, and ten unknown PV cell parameters are presented in these PV cell models. Applications are employed on two types of PV solar cells: the 57 mm diameter RTC Company of France commercial silicon for single- and double-diode models and multi-crystalline PV solar module CS6P-240P for the three-diode model. The total deviations between the actual and estimated result are used in this study as the objective function. The performance measures used in comparisons are the RMSE and relative error. The performance of EHO and the proposed three improved EHO algorithms are evaluated against the well-known optimization algorithms presented in the literature. The experimental results of EHO and the three improved EHO algorithms go as planned and proved to be comparable to recent metaheuristic algorithms. The three EHO-based variants outperform all competitors for the single-diode model, and in particular, the culture-based EHO (CEHO) outperforms others in the double/three-diode model. According the studied cases, the EHO variants have low levels of relative errors and therefore high accuracy compared with other optimization algorithms in the literature.
Highlights
This paper aims to improve Elephant Herding Optimization (EHO) performance, which is under-reported in the scientific literature
Company of France to verify their performance against single- and double-diode models
The basic EHO and its three variants are compared with the results of two algorithms from [42] called Artificial Bee Swarm Optimization algorithm (ABSO) and Harmony Search (HS) algorithm
Summary
Energy is an essential component of the universe and is considered one of the forms of existence. Energy is divided into two main types (renewable energy and non-renewable energy); non-renewable energy as fossil fuels has a terrible impact on the environment. Many nations tend to use renewable energy to produce their electricity. Solar energy is one of the primary and available renewable energy sources on the planet that has no pollution and easy installation as well as being inexpensive and noise-free. The need to add renewable energy sources is increased with the dramatic changes in electricity requirements. The effective modeling of renewable energy resources is an important issue for efficient energy management [1]
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