Identifying the parameters of different configurations of photovoltaic models based on recent artificial ecosystem‐based optimization approach

Abstract

Identifying accurate and precise photovoltaic models' parameters is the primary gate in providing a proper PV system design simulate its real behavior. Therefore, this article proposed a new approach based on a recent metaheuristic algorithm of artificial ecosystem-based optimization (AEO) to identify the optimal parameters of PV cell and module models. Various PV models are considered in this work as single diode (SD), double diode (DD), and triple diode (TD)-based circuits. The analysis is performed on which are R.T.C. France silicon solar cell, FSM-25 PV module, and Canadian-Solar-(CS6P-240P) multi-crystalline solar panel with the aid of experimental data under different operating conditions. Moreover, Lambert form is employed to validate the constructed model. Furthermore, comparative analysis with Harris hawks optimizer (HHO), gray wolf optimizer (GWO), and salp swarm algorithm (SSA) is performed. Additionally, statistical analysis using the Wilcoxon signed rank test is implemented across the three series of experiments for all employed optimizers. The obtained results confirmed the competence of the proposed approach in identifying the PV cell and modules equivalent circuits' parameters. K E Y W O R D S artificial ecosystem-based optimizer, double diode PV model, PV parameters estimation, single diode PV model, three-diode model 1 | INTRODUCTION Recently, renewable energy sources (RESs) penetrated in many engineering applications as alternatives of fossil fuel sources. Solar energy is one form of RESs in which the sunlight is converted to electrical energy via solar photovoltaic (PV) panels. Different PV panels are available which are mono-crystalline, multi-crystalline, and amorphous type.

Efficient Ranking-Based Whale Optimizer for Parameter Extraction of Three-Diode Photovoltaic Model: Analysis and Validations

“…From Table 3, also, it is clear that HWOA consumes half the time as ITLBO, which was, to some extent, competitive with HWOA for the best RMSE, and subsequently, the proposed is the best since it is more converged with less computational cost. Furthermore, to more appear the effectiveness of HWOA, the error values (EV) between the measured and estimated I under both HWOA and ITLBO as the second-best algorithm are presented in Table 4 after computing using Equation (23). This table (Table 4) shows that HWOA is the nearest to 20 measured I out of 26 ones, and this affirms the proposed algorithm: HWOA could find more accurate parameter values.…”

“…The proposed RWOA and HWOA methods are experimentally utilized to define the nine unknown parameters of an SC: RTC France [23] and two different PV modules: Photowatt-PWP201 module [24], and Kyocera KC200GT [16]. Those three models are experimentally tested in this paper to extract their optimal parameter values at standard operating conditions.…”

“…Those algorithms are executed according to their parameters in the cited papers, with the difference that N and t max are respectively set to 50 and 10,000, to ensure an equitable comparison. The investigated algorithms are artificial ecosystembased optimization (AEO, 2020) [23], improved teaching-learning-based optimization (ITLBO, 2019) [20], Interior Search Algorithm (ISA, 2020) [25], HHO (2020) [10], and WOA (2016) [18]. The experiments are developed using MATLAB R2019a with 30 independent trials for each algorithm.…”

A Canis lupus inspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

“…Chen et al designed a photovoltaic cell which was used to identify the parameter using Harris hawks optimizer algorithm with chaotic drifts 96 . Yousri et al designed an artificial ecosystem based optimization technique which is used to recognize different types of parameter configuration of PV model 97 . Kumar et al have solved cross over line balanced problem using HHO technique to select good quality chromosomes to identify the presentation of the sequential construction system 98 .…”

“…96 Yousri et al designed an artificial ecosystem based optimization technique which is used to recognize different types of parameter configuration of PV model. 97 Kumar et al have solved cross over line balanced problem using HHO technique to select good quality chromosomes to identify the presentation of the sequential construction system. 98 Arora et al designed an improved Harris hawks optimizer which is used to analysis the sensitivity of load frequency control problem including wind power penetration.…”