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Transformer oil quality assessment using random forest with feature engineering

Senoussaoui Mohammed El Amine, Brahami Mostefa et Fofana Issouf. (2021). Transformer oil quality assessment using random forest with feature engineering. Energies, 14, (7), p. 1809.

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URL officielle: http://dx.doi.org/doi:10.3390/en14071809

Résumé

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

Type de document:Article publié dans une revue avec comité d'évaluation
Volume:14
Numéro:7
Pages:p. 1809
Version évaluée par les pairs:Oui
Date:24 Mars 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
Mots-clés:transformer oil, physicochemical tests, oil assessment, machine learning, features extraction, features selection, ensemble techniques, random forest, huile de transformateur, tests physico-chimiques, évaluation de l'huile, apprentissage automatique, extraction de caractéristiques, sélection de caractéristiques, techniques d'ensemble, forêt aléatoire
Déposé le:24 mars 2021 23:11
Dernière modification:24 mars 2021 23:11
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