  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.
