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A Parameter Estimation of Photovoltaic Models Using a Boosting Flower Pollination Algorithm.
Liu, Shuai; Yang, Yuqi; Qin, Hui; Liu, Guanjun; Qu, Yuhua; Deng, Shan; Gao, Yuan; Li, Jiangqiao; Guo, Jun.
Afiliação
  • Liu S; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Yang Y; Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Qin H; Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China.
  • Liu G; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Qu Y; Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Deng S; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Gao Y; Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Li J; School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Guo J; Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Sensors (Basel) ; 23(19)2023 Oct 08.
Article em En | MEDLINE | ID: mdl-37837153
An accurate and reliable estimation of photovoltaic models holds immense significance within the realm of energy systems. In pursuit of this objective, a Boosting Flower Pollination Algorithm (BFPA) was introduced to facilitate the robust identification of photovoltaic model parameters and enhance the conversion efficiency of solar energy into electrical energy. The incorporation of a Gaussian distribution within the BFPA serves the dual purpose of conserving computational resources and ensuring solution stability. A population clustering strategy is implemented to steer individuals in the direction of favorable population evolution. Moreover, adaptive boundary handling strategies are deployed to mitigate the adverse effects of multiple individuals clustering near problem boundaries. To demonstrate the reliability and effectiveness of the BFPA, it is initially employed to extract unknown parameters from well-established single-diode, double-diode, and photovoltaic module models. In rigorous benchmarking against eight control methods, statistical tests affirm the substantial superiority of the BFPA over these controls. Furthermore, the BFPA successfully extracts model parameters from three distinct commercial photovoltaic cells operating under varying temperatures and light irradiances. A meticulous statistical analysis of the data underscores a high degree of consistency between simulated data generated by the BFPA and observed data. These successful outcomes underscore the potential of the BFPA as a promising approach in the field of photovoltaic modeling, offering substantial enhancements in both accuracy and reliability.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China