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Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches

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  • Aleksandr Kulikov

    (Department of Electric Power Engineering, Power Supply and Power Electronics, Nizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin St., 24, 603115 Nizhny Novgorod, Russia)

  • Anton Loskutov

    (Department of Electric Power Engineering, Power Supply and Power Electronics, Nizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin St., 24, 603115 Nizhny Novgorod, Russia)

  • Dmitriy Bezdushniy

    (Department of Electric Power Engineering, Power Supply and Power Electronics, Nizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin St., 24, 603115 Nizhny Novgorod, Russia)

  • Ilya Petrov

    (Department of Electric Power Engineering, Power Supply and Power Electronics, Nizhny Novgorod State Technical University n.a. R.E. Alekseev, Minin St., 24, 603115 Nizhny Novgorod, Russia)

Abstract

The complication of the structure, topology and composition of the future electrical networks is characterized by difficult-to-recognize circuit-mode situations and requires modern methods for analyzing information parameters. The growing trend of digitizing signals in substations and the use of the IEC 61850 standard results in a huge amount of new data available at the nodes of the electrical network. The development and analysis of new methods for detecting and recognizing the modes of electrical networks (normal and emergency) are topical research issues. The article explores a new approach to recognizing a faulted section of an electrical network with branches by concurrently analyzing several information features and applying machine learning methods: decision tree, random forest, and gradient boosting. The application of this approach for decision-making by relay protection has not been previously implemented. Simulation modeling and the Monte Carlo method are at the heart of obtaining training samples. The results of testing the studied methods under review showed the required flexibility, the ability to use a large number of information parameters, as well as the best results of fault recognition in comparison with the distance protection relay.

Suggested Citation

  • Aleksandr Kulikov & Anton Loskutov & Dmitriy Bezdushniy & Ilya Petrov, 2023. "Decision Tree Models and Machine Learning Algorithms in the Fault Recognition on Power Lines with Branches," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5563-:d:1200473
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    References listed on IDEAS

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    1. Aleksandr Kulikov & Anton Loskutov & Dmitriy Bezdushniy, 2022. "Relay Protection and Automation Algorithms of Electrical Networks Based on Simulation and Machine Learning Methods," Energies, MDPI, vol. 15(18), pages 1-19, September.
    2. Ramin Vakili & Mojdeh Khorsand, 2022. "A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies," Energies, MDPI, vol. 15(23), pages 1-28, November.
    3. Andra Ciocan & Nadim Al Hajjar & Florin Graur & Valentin C. Oprea & Răzvan A. Ciocan & Sorana D. Bolboacă, 2020. "Receiver Operating Characteristic Prediction for Classification: Performances in Cross-Validation by Example," Mathematics, MDPI, vol. 8(10), pages 1-17, October.
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