Abstract
Constructing an equivalent circuit for the photovoltaic (PV) generating unit converging the real operation is a difficult process because of unavailability of some parameters. Many approaches have been conducted in this field; however, they have some problems in computational time and are stuck in local optima. Therefore, this study proposes a simple, robust, and efficient methodology-incorporated capuchin search algorithm (CapSA) to construct the equivalent circuit of the PV generating unit via identifying its parameters. The CapSA is selected as it is simple and requires less computational time in addition to exploration/exploitation balance that avoids local optima. The process is formulated as an optimization problem, which aims at minimizing the root mean square error (RMSE) between measured and simulated currents. A single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM) of different PV cells and panels operating at either constant or variable weather conditions are constructed. A comparison to different programmed metaheuristic approaches is conducted. The best RMSE values obtained by the proposed CapSA are 2.27804E-04, 1.3808E-04, and 1.5182E-04 for SDM, DDM, and TDM of PVW 752 cell, respectively. For the KC200GT panel, the proposed approach achieved the best fitness values of 3.4440E-04, 1.5617E-03, and 6.6008E-03 at 25°C, 50°C, and 75°C, respectively. The obtained results confirmed the superiority and competence of the proposed CapSA in constructing a reliable equivalent circuit for the PV cell/panel.
| Original language | English |
|---|---|
| Article number | 1028816 |
| Journal | Frontiers in Energy Research |
| Volume | 10 |
| DOIs | |
| State | Published - 21 Oct 2022 |
Bibliographical note
Publisher Copyright:Copyright © 2022 Ali, Fathy, Al-Dhaifallah, Abdelaziz and Ebeed.
Keywords
- PV equivalent circuit
- capuchin search algorithm
- optimization
- parameter estimation
- renewable energy
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Fuel Technology
- Energy Engineering and Power Technology
- Economics and Econometrics