Artificial neural networks technique for parameters estimation of amorphous silicon solar module

• Autorzy: Benabdelkrim Bouchra , Ghaitaoui Touhami , Benatiallah Ali , Koussa Khaled

Identyfikatory

DOI

10.15199/48.2024.05.29

Warianty tytułu

PL

Technika sztucznych sieci neuronowych do szacowania parametrów modułu słonecznego z amorficznego krzemu

• Języki publikacji: EN

Abstrakty

EN

To conduct exact performance investigations and control studies on solar PV systems, it is necessary to extract relevant circuit model characteristics. In this work, we have proposed for the identification of the parameters of the single-diode model of the amorphous PV module, a numerical modeling approach presented by the determinist based on the “Levenberg–Marquardt (LM)” gradient descent, combined with the intelligent method based on artificial neural networks (ANN) taking into account the variation of the solar radiation and the temperature of the cell, in real working conditions.

PL

Aby przeprowadzić dokładne badania wydajności i badania kontrolne systemów fotowoltaicznych, konieczne jest wyodrębnienie odpowiednich charakterystyk modelu obwodu. W pracy tej zaproponowaliśmy do identyfikacji parametrów modelu jednodiodowego amorficznego modułu PV podejście modelowania numerycznego zaprezentowane przez deterministę w oparciu o opadanie gradientowe „Levenberga-Marquardta (LM)” w połączeniu z inteligentna metoda oparta na sztucznych sieciach neuronowych (ANN) uwzględniająca zmienność promieniowania słonecznego i temperatury ogniwa w rzeczywistych warunkach pracy.

Słowa kluczowe

EN

parameters estimation; ANN model; Levenberg–Marquardt method; performance I–V curve

PL

estymacja parametrów; model ANN; metoda Levenberg-Marquardt; krzywa wydajności I–V

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 5
• Strony: 158--163
• Opis fizyczny: Bibliogr25 poz., rys., tab.

Twórcy

autor

Benabdelkrim Bouchra

• Department of Material Sciences, Faculty of Material Sciences, Mathematics and Computer Science, University of Ahmed Draia, Adrar, Algeria
• Laboratory of Energy, Environment and Systems of Information (LEESI), University Ahmed Draia Adrar, Algeria

autor

Ghaitaoui Touhami

• Sustainable Development and Information Laboratory (LDDI), Faculty of Sciences and Technology, Ahmed DRAIA University, Adrar, Algeria

autor

Benatiallah Ali

• Laboratory of Energy, Environment and Systems of Information (LEESI), University Ahmed Draia Adrar, Algeria

autor

Koussa Khaled

• Unit of Renewable Energy Research in the Saharan Environment (URERMS), Center of Renewable Energy Development (CDER), 01000 Adrar, Algeri

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).