Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers

TL;DR

This paper develops and evaluates machine learning algorithms, including ensemble methods, to automatically detect faults in nuclear power plant transformers based on dissolved gas analysis data.

Contribution

It introduces a machine learning-based approach for automatic transformer fault detection using time series gas concentration data, achieving high accuracy.

Findings

Ensemble model achieved F1-score of 0.974.

Multiple ML models were trained and combined for optimal performance.

The approach enables early fault detection in transformers.

Abstract

Power transformers are an important component of a nuclear power plant (NPP). Currently, the NPP operates a lot of power transformers with extended service life, which exceeds the designated 25 years. Due to the extension of the service life, the task of monitoring the technical condition of power transformers becomes urgent. An important method for monitoring power transformers is Chromatographic Analysis of Dissolved Gas. It is based on the principle of controlling the concentration of gases dissolved in transformer oil. The appearance of almost any type of defect in equipment is accompanied by the formation of gases that dissolve in oil, and specific types of defects generate their gases in different quantities. At present, at NPPs, the monitoring systems for transformer equipment use predefined control limits for the concentration of dissolved gases in the oil. This study describes the stages of developing an algorithm to detect defects and faults in transformers automatically using machine learning and data analysis methods. Among machine learning models, we trained Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, Neural Networks. The best of them were then combined into an ensemble (StackingClassifier) showing F1-score of 0.974 on a test sample. To develop mathematical models, we used data on the state of transformers, containing time series with values of gas concentrations (H2, CO, C2H4, C2H2). The datasets were labeled and contained four operating modes: normal mode, partial discharge, low energy discharge, low-temperature overheating.