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Transformers Fault Identification by Frequency Response Analysis using Intelligent Classifiers

Ferreira Regelii SA, Ezzaidi Hassan, Fofana Issouf et Picher Patrick. (2021). Transformers Fault Identification by Frequency Response Analysis using Intelligent Classifiers. Dans : 22nd International Symposium on High Voltage Engineering (ISH 2021), , November 21-25, 2021, Xi’an, China.

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Résumé

Failures in power transformers generates high financial loss inherent to equipment cost and power interruption. Thus, the industry has been working into developing efficient and reliable monitoring techniques with the objective of detecting incipient faults. Frequency Response Analysis (FRA) is among well-known methods to detect faults in transformers’ active parts. FRA is very sensitive to changes in the winding geometry and a comparison between healthy and faulty measurements can present deviations that leads to the fault identification. However, until now, FRA interpretation is performed with the aid of experts. The development of FRA and the spreading of its application in transformers’ diagnostics, have led studies to focus on the advancement of objective interpretation techniques. To contribute to this matter, intelligent classifiers are evaluated over its capabilities to classify transformers faults. For this purpose, a database of measurements performed on a laboratory winding model including different faults is used. Numerical indices are used as input for machine learning classifiers. This analysis indicated that the use of one individual index is appropriate for a good classification performance using Radial Basis Function neural network. Meanwhile Support-vector machine and Backpropagation neural network performed better with a combination of indices.

Type de document:Matériel de conférence (Non spécifié)
Date:21 Novembre 2021
Sujets:Sciences naturelles et génie > Génie
Sciences naturelles et génie > Génie > Génie électrique et génie électronique
Sciences naturelles et génie > Sciences appliquées
Département, module, service et unité de recherche:Départements et modules > Département des sciences appliquées > Module d'ingénierie
Unités de recherche > Centre international de recherche sur le givrage atmosphérique et l’ingénierie des réseaux électriques (CENGIVRE) > Vieillissement de l’appareillage installé sur les lignes à haute tension (ViAHT)
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Mots-clés:frequency response analysis, power transformers, condition monitoring, machine learning, numerical indices, analyse de la réponse en fréquence, transformateurs de puissance, surveillance de l'état, apprentissage automatique, indices numériques
Déposé le:16 mars 2022 23:46
Dernière modification:16 mars 2022 23:46
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