Abstract
The electric charged particles optimization (ECPO) technique is inspired by the interaction (exerted forces) between electrically charged particles. A developed version of ECPO called MECPO is suggested in this article to enhance the capability of searching and balancing the exploitation and exploration phases of the conventional ECPO. To let the search agent jumps out from the local optimum and avoid stagnation in the local optimum in the proposed MECPO, three different strategies in the interaction between ECPs are modified in conjunction with the conventional ECPO. Therefore, the convergence rate is enhanced and reaches rapidly to the optimal solution. To evaluate the effectiveness of the MECPO, it is executed on the test functions of the CEC’17. Furthermore, the MECPO technique is suggested to estimate the parameters of different photovoltaic models, such as the single-diode model (SDM), the double-diode model (DDM), and the triple-diode model (TDM). The simulation results illustrate the validation and effectiveness of MECPO in extracting parameters from photovoltaic models.
Highlights
To deal with the increase in energy shortage as well as the several disadvantages of fossil fuels, increased research studies on renewable energy sources (RES) are urgently required [1,2]
The numerical simulation of the proposed MECPO algorithm for identifying parameters of single-diode model (SDM), diode model (DDM), and triple-diode model (TDM) is illustrated
This paper has proposed an improved version of the electric charged particles optimization (ECPO) called MECPO to solve global optimization problems well
Summary
To deal with the increase in energy shortage as well as the several disadvantages of fossil fuels, increased research studies on renewable energy sources (RES) are urgently required [1,2]. In many works of literature, the SDM and DDM are generally employed, in which the researchers have used several techniques to estimate the parameters of the PV model based on analytical and numerical methods and optimization techniques [10,11]. This new algorithm is utilized for extracting the PV module parameters. The rest of the article is organized as follows: Section 2 of the paper analyzes the problem formulation, including SDM, DDM, and TDM, and presents the objective function for identifying the parameters of the solar PV module. The PV models inclu3dofe34the sing model (SDM), the double-diode model (DDM), and the triple-diode model
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