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Relay Protection and Automation Algorithms of Electrical Networks Based on Simulation and Machine Learning Methods

Author

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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, 603950 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, 603950 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, 603950 Nizhny Novgorod, Russia)

Abstract

The tendencies and perspective directions of development of modern digital devices of relay protection and automation (RPA) are considered. One of the promising ways to develop protection and control systems is the development of fundamentally new algorithms for recognizing emergency modes. They work in accordance with the triggering rule, which is formed after processing the results of model experiments. These algorithms are able to simultaneously control a large number of features or mode parameters (current, voltage, resistance, phase, etc.). Thus, the algorithms are multidimensional. This approach in RPA becomes available since the computing power of modern processors is quite enough to process the required amount of statistical data on the parameters of possible normal and emergency operation modes of electrical network sections. The application of classical machine learning algorithms in RPA tasks is analyzed, in particular, methods of k-nearest neighbors, logistic regression, and support vectors. The use of specialized trainable triggering elements is studied both for building new protections and for improving the sophistication of traditional types of relay protection devices. The developed triggering elements of the multi-parameter RPA contribute to an increase in the sensitivity and recognition of accidents. The proposed methods for recognizing emergency modes are appropriate for implementation in intelligent electronic devices (IEDs) of digital substations.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6525-:d:908762
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    References listed on IDEAS

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    1. Olga Akhmedova & Anatoliy Soshinov & Farit Gazizov & Svetlana Ilyashenko, 2021. "Development of an Intelligent System for Distance Relay Protection with Adaptive Algorithms for Determining the Operation Setpoints," Energies, MDPI, vol. 14(4), pages 1-20, February.
    2. Razavi, Seyed-Ehsan & Rahimi, Ehsan & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Impact of distributed generation on protection and voltage regulation of distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 157-167.
    3. Aushiq Ali Memon & Kimmo Kauhaniemi, 2020. "An Adaptive Protection for Radial AC Microgrid Using IEC 61850 Communication Standard: Algorithm Proposal Using Offline Simulations," Energies, MDPI, vol. 13(20), pages 1-31, October.
    4. Tahir Khurshaid & Abdul Wadood & Saeid Gholami Frakoush & Tae-Hwan Kim & Ki-Chai Kim & Sang-Bong Rhee, 2022. "Optimal Allocation of Directional Relay for Efficient Energy Optimization in a Radial Distribution System," Energies, MDPI, vol. 15(13), pages 1-17, June.
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    Cited by:

    1. 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.
    2. Yasar Beyazit Yoldas & Recep Yumurtacı, 2023. "Improvement of Distance Protection with SVM on PV-Fed Transmission Lines in Infeed Conditions," Energies, MDPI, vol. 16(6), pages 1-18, March.
    3. Ali Vafadar & Maryam A. Hejazi & Hamed Hashemi-Dezaki & Negin Mohagheghi, 2023. "Optimal Protection Coordination of Active Distribution Networks Using Smart Selection of Short Circuit Voltage-Based Relay Characteristics," Energies, MDPI, vol. 16(14), pages 1-18, July.

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