Classifying transformer winding fault type, location and extent using FRA based on support vector machine

• Autorzy: Ezziane Hassane , Houassine Hamza , Moulahoum Samir , Chaouche Moustafa Sahnoune

Identyfikatory

DOI

10.15199/48.2022.01.04

Warianty tytułu

PL

Klasyfikacja typu, lokalizacji i zakresu uszkodzenia uzwojenia transformatora za pomocą FRA w oparciu o maszynę wektora nośnego

• Języki publikacji: EN

Abstrakty

EN

In this paper, four common winding faults in power transformers (axial displacement (AD), serial capacitance variation (VSC), ground capacitance variation (VGC), open circuit (OC)) are simulated on a transformer winding model to classify the fault type, location and extent, by applying an intelligent methodology for diagnosing transformer faults, depends on building a comprehensive database by collecting Frequency Responses Analysis (FRA) related to health and faulty conditions and analyzing them using statistical and mathematical indicators, this base that can inventory all possible faults in terms of location and extent, which is used to train a support vector machine (SVM) classifier on the faults included in it, which is then able to classify any new data . The results of the tests showed that the proposed method is characterized by high accuracy in detecting the type of defect, determining its location and the extent of its occurrence, It also contributes to the development of the application of machine learning on transformers.

PL

W tym artykule symulowane są cztery typowe uszkodzenia uzwojeń w transformatorach mocy (przemieszczenie osiowe (AD), szeregowa zmiana pojemności (VSC), zmiana pojemności uziemienia (VGC), obwód otwarty (OC)) na modelu uzwojenia transformatora w celu sklasyfikowania typu zwarcia , lokalizacji i zasięgu, poprzez zastosowanie inteligentnej metodologii diagnozowania uszkodzeń transformatorów, polega na zbudowaniu kompleksowej bazy danych poprzez zbieranie Analizy Odpowiedzi Częstotliwości (FRA) związanej ze stanami zdrowia i wadliwymi oraz analizowanie ich za pomocą wskaźników statystycznych i matematycznych, tej bazy, która może inwentaryzować wszystkie możliwych błędów pod względem lokalizacji i zasięgu, który jest używany do trenowania klasyfikatora maszyny wektora nośnego (SVM) na zawartych w nim błędach, który jest następnie w stanie sklasyfikować dowolne nowe dane. Wyniki badań wykazały, że proponowana metoda charakteryzuje się dużą dokładnością w wykrywaniu rodzaju defektu, określaniu jego lokalizacji oraz zasięgu jej występowania, przyczynia się również do rozwoju zastosowania uczenia maszynowego na transformatorach.

Słowa kluczowe

EN

frequency response analysis; FRA; support vector machine; SVM; winding fault; diagnostic

PL

odpowiedź częstotliwościowa; uszkodzenie uzwojenia; FRA

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2022
• Tom: R. 98, nr 1
• Strony: 23--33
• Opis fizyczny: Bibliogr.45 poz., rys., tab.

Twórcy

autor

Ezziane Hassane

• Laboratory of Electrical Engineering and Automatics LREA, Department of Electrical Engineering, University of Yahia Fares, Medea, Algeria
• Department of Electrical Engineering, University of Bouira, Algeria

autor

Houassine Hamza

• Laboratory of Electrical Engineering and Automatics LREA, Department of Electrical Engineering, University of Yahia Fares, Medea, Algeria

autor

Moulahoum Samir

• Laboratory of Electrical Engineering and Automatics LREA, Department of Electrical Engineering, University of Yahia Fares, Medea, Algeria

autor

Chaouche Moustafa Sahnoune

• Laboratory of Electrical Engineering and Automatics LREA, Department of Electrical Engineering, University of Yahia Fares, Medea, Algeria

Bibliografia

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