Machine Learning Approach Application for High-voltage Instrument Transformers Technical State Assessment

Abstract

This paper describes the possibilities of machine learning application in the tasks of technical state assessment of high-voltage instrument transformers. An analytical review of modern systems for technical state assessment of high-voltage equipment is presented, their advantages and disadvantages are described. A mathematical model of an automated system for assessing the high-voltage instrument current and voltage transformers based on gradient boosting over decision trees has been developed. The efficiency of the developed solution is proved using the example of analysis of a real distribution zone, which allows identifying the state of instrument transformers with an accuracy of 84%.

Machine learning application for power systems reliability assessment

“…In power systems, machine learning methods show effectiveness in solving problems of short-term forecasting of electrical energy consumption [3,4,5], in problems of monitoring and predictive diagnostics of electrical equipment [6,7,8,9], in problems of emergency control and relay protection [10], etc. It is also worth noting the experience of using machine learning methods in creating a software and hardware complex for recognizing the state and readings of electricity meters [11], the experience of developing electric power quality analyzers with a built-in artificial neural network [12], the development of the information system for identifying the power and electricity imbalances in electrical distribution networks [13].…”