Open Access
Issue
E3S Web Conf.
Volume 461, 2023
Rudenko International Conference “Methodological Problems in Reliability Study of Large Energy Systems“ (RSES 2023)
Article Number 01032
Number of page(s) 5
DOI https://doi.org/10.1051/e3sconf/202346101032
Published online 12 December 2023
  1. Concept of Digital Transformation 2030, PJSC Rosseti. https://www.rosseti.ru/sustainable-development/digital-transformation-2030/ [Google Scholar]
  2. Steven W. Knox. Machine Learning: a Concise Introduction. – Wiley – 2018. [CrossRef] [Google Scholar]
  3. Kychnik A.V. and others. Forecasting power consumption in aggregated groups, ensemble models and selective choice models. – Energy of a single network, No. 1 (56). – 2021. [Google Scholar]
  4. Tomin N.V., Kornilov V.N., Kurbatsky V.G. Increasing the efficiency of hourly forecasting of electricity consumption using machine learning models using the example of the Irkutsk energy system. -Electricity. Transmission and distribution. – No. 1(70), 2022. [Google Scholar]
  5. Tanfilyev O.V., Sidorova A.V., Cheremnykh A.A. Application of ensembles of decision trees and linear regression for operational load forecasting. – Electricity. Transmission and distribution. – No. 6(69), 2021. [Google Scholar]
  6. Goffman A.V. and others. Machine intelligence technologies for monitoring power transformers. – Energy of a single network. – No. 4 (53), 2020. [Google Scholar]
  7. Zakharov O.A. Diagnostics of high-voltage rotating electrical equipment using the example of a generator. PRANA system for generators. – Energy of a single network. – No. 4 (53), 2020. [Google Scholar]
  8. Khalyasmaa A.I. Application of machine learning methods to identify the technical condition of oil-filled instrument transformers. – Electricity. Transmission and distribution. – No. 6(63), 2020. [Google Scholar]
  9. Kulikov A.L. Using machine learning and artificial neural networks to recognize turn faults in power transformers. - Electricity. – No. 10, 2022. [Google Scholar]
  10. Kulikov A.L. and others. Formation of generalized information signs to increase the recognition of emergency modes by relay protection and automation. – Relay protection and automation. – No. 1, 2023. [Google Scholar]
  11. Hardware and software complex for recognizing the state and readings of electricity meters based on artificial intelligence algorithms. – Electricity. Transmission and distribution. – No. 1(76), 2023. [Google Scholar]
  12. Breakthrough forecasting technologies in the electric power industry using power quality analyzers with an artificial neural network. – Electricity. Transmission and distribution. – No. 4(23), 2021. [Google Scholar]
  13. Turkina O.V., Voltov I.P., Ivanov D.S., Shcherbakov M.V. Information system for determining imbalances in the distribution network. – Energy of a single network. – No. 5-6 (60-61), 2021. [Google Scholar]
  14. Boyarkin D.A., Krupenev D.S., Yakubovsky D.V. Using machine learning methods in assessing the reliability of electric power systems using the Monte Carlo method. – Bulletin of the South Ural State University. Series: Mathematical modeling and programming. – 2018. [Google Scholar]
  15. Senyuk M. Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. – Modeling and Simulation for the Electrical Power System, No. 11(3), 2023. [Google Scholar]
  16. Xin Li. Deep learning-based transient stability assessment frame-work for large-scale modern power system. – International Journal of Electrical Power & Energy Systems, No. 139(6), 2022. [Google Scholar]
  17. Kamruzzaman Md. A convolutional neural network-based ap-proach to composite power system reliability evaluation. – International Journal of Electrical Power & Energy Systems, Vol. 135, 2022. [Google Scholar]
  18. Tomin N., Sidorov D., Zhukov A. Machine Learning Techniques for Power System Security Assessment. – IFAC, 2016. [Google Scholar]
  19. Yahui Li. Online Static Security Assessment of Power Systems Based on Lasso Algorithm. – Developing and Implementing Smart Grids: Novel Technologies, Techniques and Models, No. 8(9), 2018. [Google Scholar]
  20. Mollaiee A. Novel continuous learning scheme for online static security assessment based on the weather-dependent security index. – IET Generation, Transmission & Distribution, 2022. [Google Scholar]
  21. Zhukov A. Methods of classification and restoration of regression based on compositions of decision trees with application to the problem of assessing the operational reliability of EPS. – Dissertation for the degree of candidate of technical sciences. – Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences, 2021. [Google Scholar]
  22. Ramirez-Gonzalez M. Convolutional neural nets with hyperparameter optimization and feature importance for power system static security assessment. – Electric Power Systems Research, 2022. [Google Scholar]
  23. Wu W. A Markov chain-based model for forecasting power system cascading failures. – Automation of Electric Power Systems, No. 37(5), 2013. [Google Scholar]
  24. Singh M. Tree-Based Ensemble Machine Learning Techniques for Power System Static Security Assessment. – Electric Power Components and Systems, No. 50, 2022. [Google Scholar]
  25. Avouris N. Power Systems Contingency Analysis using Artificial Neural Networks. – Proceedings of the 4th international workshop on computer science and information technologies, 2002. [Google Scholar]
  26. Elemary A. Power System Voltage Stability Index. – International Journal of Engineering Research & Technology (IJERT), Vol. 11, 2022. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.