Neural Multi-agent-based Approach for Preventing Blackouts in Power Systems

Michael Negnevitsky, Nikita Tomin, Daniil Panasetsky, Ulf Haeger, Nikolay Voropai, Christian Rehtanz, Victor Kurbatsky

Abstract

A neural multi-agent-based approach for system monitoring and preventing large-scale emergencies in power systems is presented in this paper. The automatic emergency control process is represented as a neural multi-agent system with hierarchical architecture. The proposed system consist of two main parts: the alarm trigger, a Kohonen neural network-based system for early detection of possible alarm states in a power system, and the competitive–collaborative multi-agent control system. For demonstration purposes, we investigated conventional and neural multi-agent automatic control schemes. Results are presented and discussed.

References

  1. IEEE PES PSDP Task Force on Blackout experience, mitigation, and role of new technologies, blackout experiences and lessons, Best practices for system dynamic performance, and the role of new technologies, IEEE Special Publication 07TP190, July 2007.
  2. Wang X., Shao W., Vittal V., 2005. Adaptive corrective control strategies for preventing power system blackouts, in Proc. of 15th PSCC, Liege.
  3. Lehnhoff S., Häger U., Zimmermann T., Rehtanz C., 2011. Autonomous distributed coordination of fast power flow controllers in transmission networks, in IEEE PES ISGT 2011 Europe, Manchester, UK.
  4. Häger U., 2012. Agent-based real-time Coordination of Power Flow Controllers, Dissertation, TU-Dortmund University.
  5. Panasetsky D.A., Voropai N.I., 2009. A multi-agent approach to coordination of different emergency control devices against voltage collapse. IEEE Bucharest PowerTech.
  6. Negnevitsky M., 2008. Computational Intelligence Approach to Crisis Management in Power Systems, Int. Journal of Aut. and Control, 2 (2/3), 247-273.
  7. Kalyani S., Shanti Swarup K., 2012. Design of pattern recognition system for static security assessment and classification, Pattern Analysis & Applications, vol. 15, 299-311.
  8. Niebur D. 1994. Kohonen self-organizing neural network for power system security assessment, Thesis #1244, Lausanne, EPFL.
  9. Negnevitsky M., Tomin N., Panasetsky D., Kurbatsky V., 2013. Intelligent Approach for Preventing Large-Scale Emergencies in Electric Power Systems, IEEE PowerTech 2013, Grenoble, France.
  10. Negnevitsky M., Voropai N., Kurbatsky V., Tomin N., Panasetsky D., 2013. Development of an Intelligent System for Preventing Large-Scale Emergencies in Power Systems, IEEE/PES GM, Vancouver, BC, Canada.
  11. IEEE PES CAMS Task Force on Understanding, Prediction, Mitigation and Restoration of Cascading Failures “Initial Review of Methods for Cascading Failure Analysis in Elect. Power Trans. Syst.,” In Proc. IEEE PES GM, Pittsburgh, PA USA July 2008.
Download


Paper Citation


in Harvard Style

Negnevitsky M., Tomin N., Panasetsky D., Haeger U., Voropai N., Rehtanz C. and Kurbatsky V. (2014). Neural Multi-agent-based Approach for Preventing Blackouts in Power Systems . In Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-015-4, pages 565-570. DOI: 10.5220/0004906505650570


in Bibtex Style

@conference{icaart14,
author={Michael Negnevitsky and Nikita Tomin and Daniil Panasetsky and Ulf Haeger and Nikolay Voropai and Christian Rehtanz and Victor Kurbatsky},
title={Neural Multi-agent-based Approach for Preventing Blackouts in Power Systems},
booktitle={Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2014},
pages={565-570},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004906505650570},
isbn={978-989-758-015-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Neural Multi-agent-based Approach for Preventing Blackouts in Power Systems
SN - 978-989-758-015-4
AU - Negnevitsky M.
AU - Tomin N.
AU - Panasetsky D.
AU - Haeger U.
AU - Voropai N.
AU - Rehtanz C.
AU - Kurbatsky V.
PY - 2014
SP - 565
EP - 570
DO - 10.5220/0004906505650570