Machine Learning Application for the High-Voltage Equipment Lifecycle Forecasting

This paper presents the model of the intelligent lifecycle management system for high-voltage power network equipment. The model is validated by providing technical state estimates for 110 kV oil-filled power transformers based on the technical diagnostic data retrospective. The transformers are operated at the substations of the regional power system. The problem of assessing the power equipment technical state is addressed from the point of view of mathematics as a classification problem, while the problem of technical state prediction is considered as a regression task. In the framework of the author's research, the adaptive system for managing and forecasting of the power equipment lifecycle based on machine learning methods was developed, where gradient boosting over decision trees is used as the core mathematical algorithm for solving the above problems: both for classification and regression ones.