Parameter estimation of photovoltaic module relied on golden jackal optimization

• Autorzy: Nguyen Thuan Thanh

Identyfikatory

DOI

10.24425/aee.2023.147422

• Języki publikacji: EN

Abstrakty

EN

Due to the nonlinear current-voltage (I-V) relationship of the photovoltaic (PV) module, building a precise mathematical model of the PV module is necessary for evaluating and optimizing the PV systems. This paper proposes a method of building PV parameter estimation models based on golden jackal optimization (GJO). GJO is a recently developed algorithm inspired by the idea of the hunting behavior of golden jackals. The explored and exploited searching strategies of GJO are built based on searching for prey as well as harassing and grabbing prey of golden jackals. The performance of GJO is considered on the commercial KC200GT module under various levels of irradiance and temperature. Its performance is compared to well-known particle swarm optimization (PSO), recent Henry gas solubility optimization (HGSO) and some previous methods. The obtained results show that GJO can estimate unknown PV parameters with high precision. Furthermore, GJO can also provide better efficiency than PSO and HGSO in terms of statistical results over several runs. Thus, GJO can be a reliable algorithm for the PV parameter estimation problem under different environmental conditions.

Słowa kluczowe

EN

golden jackal optimization; henry gas solubility optimization; particle swarm optimization; PV parameter estimation; single diode model

• Wydawca: Polish Academy of Sciences, Committee on Electrical Engineering
• Czasopismo: Archives of Electrical Engineering
• Rocznik: 2023
• Tom: Vol. 72, nr 4
• Strony: 987--1003
• Opis fizyczny: Bibliogr. 38 poz., fig., tab.

Twórcy

autor

Nguyen Thuan Thanh

• Faculty of Electrical Engineering Technology Industrial University of Ho Chi Minh City No. 12 Nguyen Van Bao, Ward 4, Go Vap District, Ho Chi Minh City, Vietnam

• https://orcid.org/0000-0002-6351-475X

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).