Data Mining Applied to Decision Support Systems for Power Transformers’ Health Diagnostics

Alexandra I. Khalyasmaa, Pavel V. Matrenin, Stanislav A. Eroshenko, Vadim Z. Manusov, Andrey M. Bramm and Alexey M. Romanov
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Alexandra I. Khalyasmaa: Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
Pavel V. Matrenin: Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
Stanislav A. Eroshenko: Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
Vadim Z. Manusov: Industrial Power Supply Systems Department, Novosibirsk State Technical University, 630073 Novosibirsk, Russia
Andrey M. Bramm: Ural Power Engineering Institute, Ural Federal University Named after the First President of Russia B.N. Yeltsin, 620002 Ekaterinburg, Russia
Alexey M. Romanov: Institute of Artificial Intelligence, MIREA-Russian Technological University, 119454 Moscow, Russia

Mathematics, 2022, vol. 10, issue 14, 1-25

Abstract: This manuscript addresses the problem of technical state assessment of power transformers based on data preprocessing and machine learning. The initial dataset contains diagnostics results of the power transformers, which were collected from a variety of different data sources. It leads to dramatic degradation of the quality of the initial dataset, due to a substantial number of missing values. The problems of such real-life datasets are considered together with the performed efforts to find a balance between data quality and quantity. A data preprocessing method is proposed as a two-iteration data mining technology with simultaneous visualization of objects’ observability in a form of an image of the dataset represented by a data area diagram. The visualization improves the decision-making quality in the course of the data preprocessing procedure. On the dataset collected by the authors, the two-iteration data preprocessing technology increased the dataset filling degree from 75% to 94%, thus the number of gaps that had to be filled in with the synthetic values was reduced by 2.5 times. The processed dataset was used to build machine-learning models for power transformers’ technical state classification. A comparative analysis of different machine learning models was carried out. The outperforming efficiency of ensembles of decision trees was validated for the fleet of high-voltage power equipment taken under consideration. The resulting classification-quality metric, namely, F 1 -score, was estimated to be 83%.

Keywords: power transformer; equipment technical state; identification of technical condition; machine learning applications; feature engineering; data preprocessing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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