ASSESSMENT OF THE ELECTRICAL EQUIPMENT INSULATION STATE USING THE GRADIENT BOOSTING ALGORITHM

Abstract

The creation of analytical software products aimed at assessing the electrical equipment state has become a priority in the development of diagnostics in the power industry. The artificial intelligence methods are useful for this problem-solving. In the article, we propose a method for analyzing the monitoring data of partial discharges in the insulation of electrical equipment using machine-learning technologies. An analytical assessment of the partial discharges characteristics allows us to conclude on the insulation state of the object. It is proposed to use integrated diagnostic parameters, such as partial discharges intensity – the maximum measured value of the apparent charge of a single, repetitive and regular partial discharges. The total sample is characterized by an imbalance, which is typical for technical diagnostics in general. Among machine learning algorithms, bagging and boosting have proven to be the most effective. The mathematical apparatus of gradient boosting is considered in the example of the most common algorithms GBM (Gradient Boosting Machine) and CatBoost. The model was created in the Python programming language. The model created on the basis of the CatBoost algorithm was used for assessing the condition of the oil insulation of power transformers. The model’s accuracy of 68.85% was achieved after optimizing the parameters of the CatBoost algorithm. The article concluded that it is necessary to increase the training sample size and improve its balance. It is inadvisable to interpret the predicted data in the field of diagnostics parameters at the available accuracy of the model’s wok.