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

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, F1-score, was estimated to be 83%.

Review of Modeling Approaches for Conjugate Heat Transfer Processes in Oil-Immersed Transformers

“…AI-based models require a large volume and completeness of the data, as the observed parameters of deep retrospective allow for correct generalization and interpretation of the results. In work [7], the authors describe the problem of power transformers' technical state assessment based on data preprocessing and machine learning. The drawbacks of DT based on physical modeling include the complexity of mathematically describing the physical processes of various nature occurring in transformers.…”

“…For this reason, the presented review is limited to considering the modeling of the electromagnetic problem and the problem of coupled heat transfer as the main phenomena describing the operation and state of an oil-immersed transformer. This choice can be justified by a significant number of works [6,7,[15][16][17][18][19][20][21][22][23], dedicated to assessing the state of the transformer and aging of insulation, where the temperature distribution and the values of the hottest points are of key importance. As illustrative example, the work [17] is devoted to the study of the degradation of insulating paper under the action of a non-uniform temperature field.…”