Szczegóły publikacji
Opis bibliograficzny
Global maximum power point tracking (GMPPT) control method of solar photovoltaic system under partially shaded conditions / Mohammad ABU SARHAN, Andrzej BIEŃ, Szymon BARCZENTEWICZ, Rana HASSAN // W: ICARA 2022 [Dokument elektroniczny] : 8th International Conference on Automation, Robotics and Applications : 18–20 February 2022 : virtual conference. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — (International Conference on Automation, Robotics and Applications ; ISSN 2767-7745). — ISBN USB version: 978-1-6654-8382-7, Print on Demand ISBN: 978-1-6654-8384-1. — e-ISBN: 978-1-6654-8383-4. — S. 209–216. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 215–216, Abstr. — Publikacja dostępna online od: 2022-03-22
Autorzy (4)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139667 |
|---|---|
| Data dodania do BaDAP | 2022-04-05 |
| Tekst źródłowy | URL |
| DOI | 10.1109/ICARA55094.2022.9738531 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | International Conference on Automation, Robotics and Applications |
Abstract
When PV system operates under partial shading condition (PSC), the P-V characteristics curve of PV system will have different local maximum points. The existence of several peaks on the PV characteristics curve causes more complexity to extract the global maximum point under these conditions. Therefore, proposing an intelligent MPPT tracker which presents an efficient prediction of the global maximum power point from PV system under uniform or non-uniform solar irradiation levels is the key purpose of this paper. The desired tasks are accomplished efficiently by using this intelligent tracker which is based on two inputs-one output adaptive neuro-fuzzy inference system (ANFIS). The inputs of ANFIS controller are the PV system voltage and current respectively while, the output is the maximum voltage. The training of ANFIS network is accomplished using a database analyzed for various partial shading scenarios extracted from the simulation platform by examining the PV system at different levels of radiation and temperature, then the optimal maximum voltage at each specific level of radiation and temperature was obtained to be used as training data. The proposed method is examined under three scenarios for partial shading condition in order to investigate its effectiveness adequately. The results ensure that the proposed tracker can distinguish between global and local maximum power points of PV system effectively and robustly. The validity of this intelligent proposed method is implemented using MATLAB/Simulink environment.