Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Mohammed El Amine Senoussaoui, Mostefa Brahami and Issouf Fofana
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Mohammed El Amine Senoussaoui: Faculty of Sciences and Technology, University of Mascara, Route de Mamounia, Mascara BP 305-29000, Algeria
Mostefa Brahami: Intelligent Control and Electrical Power Systems Laboratory, Faculty of Electrical Engineering, Djilali Liabes University of Sidi Bel Abbes, Sidi Bel Abbes BP 89-22000, Algeria
Issouf Fofana: Research Chair on the Aging of Power Network Infrastructure (ViAHT), University of Quebec in Chicoutimi, Chicoutimi, QC G7H 2B1, Canada

Energies, 2021, vol. 14, issue 7, 1-15

Abstract: 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.

Keywords: transformer oil; physicochemical tests; oil assessment; machine learning; features extraction; features selection; ensemble techniques; random forest (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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