Transformer Life Prediction Based on Failure Probability and Health Index

In power systems, transformers play a central role in ensuring the efficient transmission and distribution of electrical energy. However, over time and with continued use, transformer performance degrades, increasing the risk of system failure. This performance degradation not only affects the reliability and stability of the system, but can also lead to costly maintenance and sudden power interruptions. Therefore, accurate prediction of the remaining life of a transformer, as well as timely implementation of maintenance and replacement programs, is critical to maintaining the continuity and stability of the power system. Traditional transformer life prediction methods often rely on periodic physical inspections, which is not only time-consuming and labor-intensive, but also difficult to accurately capture the dynamic nature of transformer degradation. Therefore, the development of a more accurate and automated prediction model has become a research priority. To predict the remaining life of the transformer, this paper proposes a prediction method based on failure probability. First, data processing is performed on the transformer failure probability and health index. Then, the processed data is fitted using the least squares method, and a transformer failure probability model is established. A mapping between the health index and failure probability of transformers is proposed, which is used to obtain a set of health index data for the transformer. Finally, polynomial regression is used to predict the remaining life of a transformer.