Power Outage Prediction on Overhead Power Lines on the Basis of Their Technical Parameters: Machine Learning Approach

Vadim Bol’shev (), Dmitry Budnikov, Andrei Dzeikalo and Roman Korolev
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Vadim Bol’shev: Laboratories of Power Supply, Electrical Equipment and Renewable Energy, Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Dmitry Budnikov: Laboratories of Electrical, Thermal Technologies and Energy Saving, Federal Scientific Agroengineering Center VIM, 109428 Moscow, Russia
Andrei Dzeikalo: Independent Researcher, Houston, TX 77077, USA
Roman Korolev: Independent Researcher, 196191 St. Petersburg, Russia

Energies, 2025, vol. 18, issue 18, 1-20

Abstract: In this study, data on the characteristics of overhead power lines of high voltage was used in a classification task to predict power supply outages by means of a supervised machine learning technique. In order to choose the most optimal features for power outage prediction, an Exploratory Data Analysis on power line parameters was carried out, including statistical and correlational methods. For the given task, five classifiers were considered as machine learning algorithms: Support Vector Machine, Logistic Regression, Random Forest, and two gradient-boosting algorithms over decisive trees LightGBM Classifier and CatBoost Classifier. To automate the process of data conversion and eliminate the possibility of data leakage, Pipeline and Column Transformers (builder of heterogeneous features) were applied; data for the models was prepared using One-Hot Encoding and standardization techniques. The data were divided into training and validation samples through cross-validation with stratified separation. The hyperparameters of the classifiers were adjusted using optimization methods: randomized and exhaustive search over specified parameter values. The results of the study demonstrated the potential for predicting power failures on 110 kV overhead power lines based on data on their parameters, as can be seen from the derived quality metrics of tuned classifiers. The best quality of outage prediction was achieved by the Logistic Regression model with quality metrics ROC AUC equal to 0.78 and AUC-PR equal to 0.68. In the final phase of the research, an analysis of the influence of power line parameters on the failure probability was made using the embedded method for determining the feature importance of various models, including estimating the vector of regression coefficients. It allowed for the evaluation of the numerical impact of power line parameters on power supply outages.

Keywords: electrical network; power line; power supply reliability; power outage; power line failure; machine learning; feature importance; support vector machine; logistic regression; random forest; gradient boosting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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