An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models
Energy is one of the most important requirements for countries and serves as an indicator of the development level of a country. Developments in social, economic and industrial areas have increased the energy needs of countries. Solar energy is one of the most promising renewable energy sources to m...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2022
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/b3f7ce4a5a34488883e1c5bb4949371d |
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| Sumario: | Energy is one of the most important requirements for countries and serves as an indicator of the development level of a country. Developments in social, economic and industrial areas have increased the energy needs of countries. Solar energy is one of the most promising renewable energy sources to meet increased energy demands. Photovoltaic (PV) models used to generate electricity from solar energy are among the most popular renewable energy systems. To obtain maximum efficiency from PV models, it is very important to estimate parameters based on current–voltage data, simulate and control PV models for electricity generation. To achieve maximum efficiency for PV models, their parameters should be estimated in an optimum way. Therefore, tree seed algorithm (TSA) developed inspired by nature was proposed in this study. TSA is a meta-heuristic algorithm inspired by the connection between trees and seeds and proposed for solving continuous optimization problems. Parameters of three different PV models (single diode model, double diode model and PV module) were optimized with TSA. Various analyses were performed to prove the success of the proposed method in parameter optimization of PV models. The results were also compared with those of similar studies in the literature. The analysis results indicated that the proposed method yielded very successful, effective and reliable results, and it was concluded that it was a competitive method compared to other methods in the literature. |
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