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Frequency response analysis interpretation using numerical indices and machine learning: A case study based on a laboratory model

De Andrade Ferreira R. S., Picher Patrick, Ezzaidi Hassan et Fofana Issouf. (2021). Frequency response analysis interpretation using numerical indices and machine learning: A case study based on a laboratory model. IEEE Access, 9, p. 67051-67063.

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URL officielle: http://dx.doi.org/doi:10.1109/ACCESS.2021.3076154

Résumé

Frequency response analysis is a powerful tool for mechanical fault diagnostics in power transformers. However, interpretation schemes still today depend on expert analyses, mainly because of the complex structure of power transformers. One of the fundamental shortcomings of experimental investigations is that mechanical deformations cannot be managed on real transformers to obtain data for different scenarios because they are too destructive. To address this issue in a systematic way, the current research used a specially designed laboratory transformer model that allows mechanical defects to be introduced so its frequency response can be evaluated under different conditions. The key feature of this model is the non-destructive interchangeability of its winding sections, allowing reproducibility and repeatability of frequency response measurements. Numerical indices were compared over key performance indicators (linearity, sensitivity and monotonicity). The analysis indicated that comparative standard deviation offered promising results for evaluation of mechanical deformations on the laboratory winding model given its monotonic behaviour, sensitivity and linear increase with fault severity. Additionally, support vector machine learning, radial basis function neural network and the statistical k-nearest neighbour method were used for fault classification with different strategies and configurations. While limited data from different transformers are used in the available literature, the approach discussed here considers 371 measurements from the same transformer model. The test results are supportive and demonstrate great accuracy when machine learning is used for winding fault classification.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:9
Pages:p. 67051-67063
Version évaluée par les pairs:Oui
Date: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)
Unités de recherche > Centre international de recherche sur le givrage atmosphérique et l’ingénierie des réseaux électriques (CENGIVRE)
Mots-clés:frequency response, windings, power transformer insulation, numerical models, current measurement, indexes, frequency measurement, réponse en fréquence, enroulements, isolation du transformateur de puissance, modèles numériques, mesure de courant, index, mesure de fréquence, condition monitoring, frequency response analysis interpretation, machine learning, numerical indices, power transformers, radial basis function, support vector machines
Déposé le:19 mai 2021 18:22
Dernière modification:17 août 2021 18:47
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