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A machine-learning approach to identify the influence of temperature on FRA measurements

Suassuna de Andrade Ferreira Regelii, Picher Patrick, Ezzaidi Hassan et Fofana Issouf. (2021). A machine-learning approach to identify the influence of temperature on FRA measurements. Energies, 14, (18), e5718.

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URL officielle: http://dx.doi.org/doi:10.3390/en14185718

Résumé

Frequency response analysis (FRA) is a powerful and widely used tool for condition assessment in power transformers. However, interpretation schemes are still challenging. Studies show that FRA data can be influenced by parameters other than winding deformation, including temperature. In this study, a machine-learning approach with temperature as an input attribute was used to objectively identify faults in FRA traces. To the best knowledge of the authors, this has not been reported in the literature. A single-phase transformer model was specifically designed and fabricated for use as a test object for the study. The model is unique in that it allows the non-destructive interchange of healthy and distorted winding sections and, hence, reproducible and repeatable FRA measurements. FRA measurements taken at temperatures ranging from −40 °C to 40 °C were used first to describe the impact of temperature on FRA traces and then to test the ability of the machine learning algorithms to discriminate between fault conditions and temperature variation. The results show that when temperature is not considered in the training dataset, the algorithm may misclassify healthy measurements, taken at different temperatures, as mechanical or electrical faults. However, once the influence of temperature was considered in the training set, the performance of the classifier as studied was restored. The results indicate the feasibility of using the proposed approach to prevent misclassification based on temperature changes.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:14
Numéro:18
Pages:e5718
Version évaluée par les pairs:Oui
Date:10 Septembre 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)
Mots-clés:frequency response analysis interpretation, transformer condition monitoring, machine learning, comparative standard deviation, support vector machine, fréquence d'interprétation de l'analyse de la réponse, surveillance de l'état du transformateur, apprentissage automatique, écart type comparatif, support de machine à vecteurs
Déposé le:20 oct. 2021 22:58
Dernière modification:20 oct. 2021 22:58
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