Detection and classification of photovoltaic system faults using neural network

• Autorzy: Moulay, Aicha , Benslimane, Tarak , Abdelkhalek, Othmane , Koussa, Khaled

Warianty tytułu

PL

Wykrywanie i klasyfikacja usterek systemów fotowoltaicznych z wykorzystaniem sieci neuronowych

• Języki publikacji: EN

Abstrakty

EN

With the growth of solar energy plants and their importance in the world, a fault diagnosis of photovoltaic systems has become an essential task to perform in order to protect the user and PV system components, in addition to increasing energy productivity. This paper presents an efficient neural network method for detecting and classifying different faults in PV system. These faults can occur in a PV array or boost converter. A simple feed forward neural network feed with meteorological parameters (Irradiance and Temperature) together with electrical data (Voltage and Current) has proven its effectiveness to identify common faults in PV system with very high accuracy. This is done by simulation in the Matlab Simulink environment.

PL

Wraz z rozwojem elektrowni słonecznych i ich znaczeniem na świecie, diagnostyka usterek systemów fotowoltaicznych stała się podstawowym zadaniem do wykonania w celu ochrony użytkownika i komponentów systemu PV, a także zwiększenia wydajności energetycznej. W artykule przedstawiono wydajną metodę sieci neuronowych do wykrywania i klasyfikacji różnych uszkodzeń w systemie PV. Te usterki mogą wystąpić w panelu fotowoltaicznym lub przetwornicy podwyższającej napięcie. Proste zasilanie sieci neuronowej ze sprzężeniem zwrotnym z parametrami meteorologicznymi (natężenie promieniowania i temperatura) wraz z danymi elektrycznymi (napięcie i prąd) dowiodło swojej skuteczności w identyfikowaniu typowych usterek w systemie fotowoltaicznym z bardzo dużą dokładnością. Odbywa się to poprzez symulację w środowisku Matlab Simulink.

Słowa kluczowe

PL

instalacja fotowoltaiczna; wykrywanie awarii; przetwornica podwyższająca napięcie; sieć neuronowa

EN

photovoltaic system; detection Fault; boost converter; neural network

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 11
• Strony: 157--162
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Moulay, Aicha

• Smart Grids and Renewable Energies Laboratory, University Tahri Mohammed of Béchar Street; PO Box 417 Béchar 08000 Béchar, Algeria,

autor

Benslimane, Tarak

• Department of Electrical Engineering, Faculty of Technology, University of M’sila,Street, City, CountryBP 166 Ichbilia, M’sila, Algeria,

autor

Abdelkhalek, Othmane

• Smart Grids and Renewable Energies Laboratory, University Tahri Mohammed of Béchar. Street; PO Box 417 Béchar 08000 Béchar, Algeria,

autor

Koussa, Khaled

• Unitée Recherche en Energies Renouvelables en Milieu Saharien, URERMS, Centre de Développement des Energies Renouvelables, CDER 01000Adrar, Algeria,

Bibliografia

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Identyfikatory

DOI

10.15199/48.2023.11.27