An efficient fuel cell maximum power point tracker based on an adaptive neural fuzzy inference system

• Autorzy: Zouggaret, Abdelhak , Azzeddine, Hocine Abdelhak , Berka, Mohammed , Chaouch, Djamel Eddine , Hebali, Mourad , Bellil, Melouka , Djilani Kobibi, Youcef Islam

Warianty tytułu

PL

Wydajny moduł śledzenia maksymalnego punktu mocy ogniwa paliwowego oparty na adaptacyjnym systemie wnioskowania neuronowego fuzzy

• Języki publikacji: EN

Abstrakty

EN

In this article, we develop a Maximum Power Point Tracker (MPPT) for a fuel cell system based on an Adaptive Neural Fuzzy Inference System (ANFIS). The system considered consists of a Proton Exchange Membrane fuel cell (PEMFC) connected to a resistive load via a boost converter, an ANFIS giving the reference signals (the voltage and the current values of the maximum power point whatever the operating conditions of the fuel cell), and a PI (Proportional integrator) controller with a Pulse Width Modulation (PWM) signal generator to tuning the duty cycle of the DC-DC boost converter. The ANFIS training database uses samples calculated using a validate fuel cell electrochemical model. The simulation results obtained using Matlab-Simulink package demonstrate the effectiveness of the proposed MPPT compared to conventional MPPT techniques in terms of static and dynamic performance.

PL

W tym artykule opracowujemy śledzenie punktu maksymalnej mocy (MPPT) dla systemu ogniw paliwowych opartego na Adaptive Neural Fuzzy Inference System (ANFIS). Rozważany system składa się z ogniwa paliwowego z membraną do wymiany protonów (PEMFC) połączonego z obciążeniem rezystancyjnym poprzez konwerter doładowania, ANFIS dający sygnały odniesienia (napięcie i wartości prądu maksymalnego punktu mocy, niezależnie od warunków pracy ogniwa paliwowego ) oraz sterownik PI (proporcjonalny integrator) z generatorem sygnału z modulacją szerokości impulsu (PWM) do dostrajania cyklu pracy przetwornicy podwyższającej DC-DC. Baza danych szkoleniowych ANFIS wykorzystuje próbki obliczone przy użyciu walidacyjnego modelu elektrochemicznego ogniw paliwowych. Wyniki symulacji uzyskane przy użyciu pakietu MATLAB-Simulink pokazują skuteczność proponowanego MPPT w porównaniu z konwencjonalnymi technikami MPPT pod względem wydajności statycznej i dynamicznej.

Słowa kluczowe

PL

ANFIS; ogniwo paliwowe z membraną do wymiany protonów; PEMFC; śledzenie punktu maksymalnej mocy; MPPT

EN

adaptive neuro fuzzy inference system; ANFIS; proton exchange membrane fuel cell; PEMFC; maximum power point tracking; MPPT

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2023
• Tom: R. 99, nr 2
• Strony: 135--139
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Zouggaret, Abdelhak

• Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Algeria,

autor

Azzeddine, Hocine Abdelhak

• LSTE Laboratory, Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Algeria,

autor

Berka, Mohammed

• Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Algeria,

autor

Chaouch, Djamel Eddine

• Faculty of Science and Technology University Mustapha Stambouli of Mascara, Algeria,

autor

Hebali, Mourad

• Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Algeria,

autor

Bellil, Melouka

• Faculty of Life and Nature Sciences, University Mustapha Stambouli of Mascara, Algeria,

autor

Djilani Kobibi, Youcef Islam

• Faculty of Science and Technology, University Mustapha Stambouli of Mascara, Algeria,

Bibliografia

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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).

Identyfikatory

DOI

10.15199/48.2023.02.23