Ensemble Machine Learning Methods for better Dynamic Assessment of Transformer Status

Abstract

Analyzing dissolved gases in the transformer's mineral oil helps to detect and classify the systemic faults in electric power transformers. Formerly, empirical methods such as Rogers ratio, Duval triangles 1-4-5, and pentagons 1-2 were used for transformer fault classification. Loose fit for every transformer type is one of the most prominent disadvantages of conventional methods. Formulating robust machine learning algorithms, such as the decision trees, can significantly overcome the loose fit issue. This paper focuses on implementing four different decision tree algorithms, including a regular decision tree classifier, a bagging classifier, a boosting classifier, and a stacking classifier to classify six different transformer fault types distinctly. Further, this study shows that the efficacy and accuracy of the four mentioned classifiers could be far exceeded when combined using a wisdom of the crowd approach. The wisdom of the crowd approach essentially merges the predicted classes from the four individual classifiers and decides on the final prediction via a hardvoting routine. The computational evaluation revealed that the given voting approach could significantly improve power transformers' online diagnostic accuracy up to 91%, thus aiding early forecast of power transformers' preventive maintenance.

Transformer Internal and Inrush Current Fault Detection Using Machine Learning

“…A supervised machine learning approach considering wind speed and signal sampled from current transformers in wind farms to distinguish normal, internal and external faults is proposed [16]. Machine learning-based decision tree classifiers are listed to differentiate six types of transformer faults [17]. Machine learning-based extended Kalman filter hybridized with a support vector machine is used to classify faults that cause false tripping of differential protection system in three-phase transformers [18].…”

Analysis of 11kV/ 430 V 500 kVA Transformer Dissolved Gas using Pre-processing Techniques through Duval’s Triangle

“…This data file is initially loaded in to the MATLAB software and converted into ensemble (13) timetable for understanding how each gas changes with respect to the time.…”

The use of predictive analysis algorithms and methods for the overhead contact line operation mode selection

“…The diagnosis and remote monitoring systems is controlling the technical conditions of the particular railway infrastructure components (transformers, overhead contact line equipment, overhead power lines, etc.) [1][2][3][4][5][6][7][8]. One of the exiting diagnosis systems' serious shortcomings is a lack of a methodological foundation for a complex analysis of diagnostic information flow, retrospective data accumulation and storage methods, which have particular importance as a raw training set data for machine learning, clusterization and classification networks.…”