Exploring Transformer Life Forecasting through an In-Depth Analysis Utilizing the Random Forest Algorithm in Research and Development

Abstract

Accurately assessing the life and operating status of transformers has important guiding significance for the formulation of maintenance strategies for power grid companies, and at the same time plays a key role in the risk management of power grid companies. However, the traditional methods for predicting the remaining life of the equipment have the problems of insufficient accuracy or long data training time. In order to achieve a more accurate assessment of the life and status of the transformer, a random forest-based transformer life prediction method is constructed in this paper. Relying on the theory of big data analysis, by mining and analyzing the accumulated data of massive transformers, the life prediction model of the transformer is established and the characteristic parameters affecting the life of the transformer are extracted to predict the life of the transformer. The experimental data research demonstrates that the model can be accurate and effective Predicting the life of transformers has higher prediction accuracy than traditional methods, providing method references for asset management and risk management of power grid companies.