Analysis of the State of High-Voltage Current Transformers Based on Gradient Boosting on Decision Trees

This paper addresses the problem of instrument current transformers technical state assessment based on machine learning methods. The introductory parts of the paper provide a detailed analysis of modern methods and approaches for technical state assessment of high-voltage power equipment of power plants and substations as well as a review of modern software tools and the latest trends in the given field of study. Justification of the relevance of the presented research aimed at instrument current transformers technical state assessment is provided along with the motivation for machine learning methods application for improvement of the accuracy and quality of high-voltage equipment state classification. Within the framework of the study, a comparative analysis of gradient boosting on decision trees and random forest algorithms was carried out for a given mathematical problem formulation. The main stages of processing the initial dataset are proposed as a step-by-step procedure, including feature extraction, feature transformation, feature interactions, etc. The outperforming efficiency of gradient boosting on decision trees algorithm was validated for real power equipment fleet. The resulting classification quality metrics of current transformers technical state assessment, Precision and Recall, are estimated to be 87.1% and 83.7%, correspondingly.

Automated summary

This paper focuses on the technical state assessment of instrument current transformers using machine learning methods, specifically gradient boosting on decision trees algorithm. The study shows that this algorithm outperforms random forest in evaluating the technical state of real power equipment, with Precision and Recall metrics estimated at 87.1% and 83.7%, respectively.