EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 139 (2019) E3S Web Conf., 139 (2019) 01051 References
Open Access
Issue
E3S Web Conf.
Volume 139, 2019
Rudenko International Conference “Methodological problems in reliability study of large energy systems” (RSES 2019)
Article Number 01051
Number of page(s) 6
DOI https://doi.org/10.1051/e3sconf/201913901051
Published online 16 December 2019
  1. Voropai N.I., Osak A.B. Electric power systems of the future // Energy policy. - 2014. - No. 5. - pp. 60-63. (in Russian) [Google Scholar]
  2. Kundur P.C. Power System Stability and Control. McGraw-Hill, 1994. [Google Scholar]
  3. Gluskin I.Z., Iofiev B.I. (with the participation of Meklin A.A., Chekalovets L.N.). Emergency automation in power systems. – M.: “Znack”. 2009. -556 p. (in Russian) [Google Scholar]
  4. Pospelov D.A. Multi-agent systems: present and future // Information Technologies and Computing Systems. - 1998. - No. 1. (in Russian) [Google Scholar]
  5. General requirements for the creation of an intelligent energy system with an active adaptive network - IES AAN / Working group of FGC UES, Team Leader V.V. Dorofeev - M., January 2012. - 88 p. (in Russian) [Google Scholar]
  6. Vasiliev S.N., Zherlov A.K., Fedosov E.A., Fedunov B.E. Intelligent control of dynamic systems. - M.: Physical and mathematical literature, 2000. - 352 p. - ISBN 5-9221-0050-5. (in Russian) [Google Scholar]
  7. The concept of the intellectual electric power system of Russia with an active and adaptive network / Ed. Fortov V.E. and Makarov A.A. - M.: NTC FGC UES, 2012. - 235 p. (in Russian) [Google Scholar]
  8. Negnevitsky M. Artificial intelligence: a guide to intelligent systems. 3rd edn, Addison Wesley, Harlow, England, 2011. 504 p. [Google Scholar]
  9. Tang Y., He H., Wen J., Liu J. Power system stability control for a wind farm based on adaptive dynamic programming // IEEE Trans. Smart Grid. 2015. Vol. 6. 2015. P.166–177 [Google Scholar]
  10. Liu Y. Machine Learning for Wind Power Prediction. MCS Thesis. University of New Brunswick, Canada. 2016. 88 p. [Google Scholar]
  11. Francois-Lavet V. et al. Deep Reinforcement Learning Solutions for Energy Microgrids Management // In European Workshop on Reinforcement Learning. 2016. [Google Scholar]
  12. Zhukov A., Tomin N., Sidorov D., Kurbatsky V., Panasetsky D. On-Line Power Systems Security Assessment Using Data Stream Random Forest Algorithm / In: Zelinka I., Vasant P., Duy V., Dao T. (eds) Innovative Computing, Optimization and Its Applications. Studies in Computational Intelligence. Vol 741. 2018. Springer. [Google Scholar]
  13. Tomin N., Negnevitsky M., Rehtanz Ch. Preventing Large-Scale Emergencies in Modern Power Systems: AI Approach // Journal of Advanced Computational Intelligence and Intelligent Informatics. 2014. Vol. 18. No.5. P. 714-727. [CrossRef] [Google Scholar]
  14. Xu Y., Zhang W., Liu W., Ferrese F. Multiagent-based reinforcement learning for optimal reactive power dispatch // IEEE Trans. Syst., Man, Cyber. 2012. Vol. 42. P. 1742–1751. [CrossRef] [Google Scholar]
  15. Mocanu E., Nguyen P.H., Gibescu M. Deep Learning for Power System Data Analysis, In Big Data Application in Power Systems. ed. by Reza Arghandeh and Yuxun Zhou, Elsevier, 2018. P. 125-158. [CrossRef] [Google Scholar]
  16. Belkacemi R., Abdulrasheed Babalola A., Zarrabian S. Real-Time Cascading Failures Prevention Through MAS Algorithm and Immune System Reinforcement Learning // Electric Power Components and Systems. 2017. Vol. 45. No. 5. P. 505-519. [CrossRef] [Google Scholar]
  17. Yousefian R., Kamalasadan K. Design and Real-Time Implementation of Optimal Power System Wide-Area System-Centric Controller Based on Temporal Difference Learning // IEEE Trans. on Industry Applications. 2015. Vol.1. [Google Scholar]
  18. Zarabbian S., Belkacemi R., Babalola A.A. Reinforcement learning approach for congestion management and cascading failure prevention with experimental application // Elec. Power Syst. Research. 2016. Vol. 141. P. 179–190. [CrossRef] [Google Scholar]
  19. Glavic M., Ernst D., and Wehenkel L. A reinforcement learning based discrete supplementary control for power system transient stability enhancement // Engineering Intelligent Systems for Electrical Engineering and Communications. 2005. Vol. 13. P. 81–88. [Google Scholar]
  20. Kaci A. et al. Synchrophasor Data Baselining and Mining for Online Monitoring of Dynamic Security Limits // IEEE Trans. on Power Systems. 2014. Vol. 29, No. 6. P. 2681-2695. [CrossRef] [Google Scholar]
  21. Ye D., Zhang M., Sutanto D. A hybrid multiagent framework with Q-learning for power grid systems restoration // IEEE Trans. Power Syst. 2011. Vol. 26. P. 2434–2441. [Google Scholar]
  22. Liu C. et al. Dynamic security assessment of western Danish power system based on ensemble decision trees // in Proc. of the 12th IET International Conference on Developments in Power System Protection. Copenhagen, 2014. [Google Scholar]
  23. Ernst D., Wehenkel L., Glavic M. Power systems stability control: Reinforcement learning framework // IEEE Trans. Power Syst. 2004. Vol. 19. P. 427–435. [Google Scholar]
  24. Kogan I., Boehme K., Herrmann H-J. The introduction of advanced machine learning technologies in the protection relay // Sat. Proceedings of the International Scientific and Technical Conference and Exhibition “Relay Protection and Automation of Energy Systems 2017”. St. Petersburg. Russia. 2017. [Google Scholar]
  25. Irpan A. Deep Reinforcement Learning Doesn’t Work Yet [Electronic Document] // Sorta Insightful. 2018. Access Mode: https://www.alexirpan.com/2018/02/14/rl-hard.html [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.