An Efficient Simulator for Fault Detection and Recovery in Smart Grids - FDIRSY

Syrine Ben Meskina, Narjes Doggaz, Mohamed Khalgui

2015

Abstract

This research paper deals with failures and faults in power smart grids. We propose an original multi-agent approach for power system recovery based on fault classification. For that, we propose the classification of faults as dominant or equivalent ones. This classification has the advantage of optimizing the task of power system recovery. To test and validate our approach, we develop a simulator, named FDIRSY (Fault Detection, Isolation and Recovery SYstem). The experimental study showed that our approach ensures the search for the best solution from the existing ones thanks to the use of mobile agents. These agents have the advantage of evaluating all the existing alternatives while reducing the communication cost (in terms of exchanged messages). We demonstrate that our approach is gainful in terms of required times, actions to be performed as well as the faults to be resolved thanks to the proposed fault classification.

References

  1. Ben Meskina, S., Doggaz, N., and Khalgui, M. (2014). New solutions for fault detections and dynamic recoveries of flexible power smart grids. In 9th International Conference ICINCO in Informatics in Control, Automation and Robotics.
  2. Calderaro, V., Hadjicostis, C. N., Piccolo, A., and Siano, P. (2011). Failure identification in smart grids based on petri net modeling. IEEE Transactions on Industrial Electronics, pages 4613 - 4623.
  3. Chertkov, M., Pan, F., and Stepanov, M. G. (2011). Predicting failures in power grids: The case of static overloads. IEEE Transactions on Smart Grid.
  4. Fang, X., Misra, S., Xue, G., and Yang, D. (2012). Smart grid - the new and improved power grid: A survey. IEEE Communications Surveys and Tutorials.
  5. Jiang, Z., Khalgui, M., Mosbahi, O., and Jaouadi, A. (2014a). A novel hierarchical multi-agent architecture for automatic restoration of smart grids. International Journal of Control and Automation.
  6. Jiang, Z., Mosbahi, O., and Khalgui, M. (2014b). A multiagent architecture for the self-healing of sgs based on iec 61499/61850. Energy Education Science and Technology Part A. Energy Science and Research.
  7. Massoud, A. and Wollenberg, B. (2005). Toward a smart grid: power delivery for the 21st century. In IEEE Power and Energy Magazine, Minneapolis, MN, USA.
  8. McArthur, S. D. J., Davidson, E. M., Catterson, V. M., Dimeas, A. L., Hatziargyriou, N. D., Ponci, F., and Funabashi, T. (2007a). Multi-agent systems for power engineering applications - part i: Concepts, approaches, and technical challenges. In IEEE Transactions on Power Systems.
  9. McArthur, S. D. J., Davidson, E. M., Catterson, V. M., Dimeas, A. L., Hatziargyriou, N. D., Ponci, F., and Funabashi, T. (2007b). Multi-agent systems for power engineering applications part ii: Technologies, standards, and tools for building multi-agent systems. In IEEE Transactions on Power Systems.
  10. Oudalova, A. and Fidigattib, A. (2009). Adaptive network protection in microgrids. International Journal of Distributed Energy Resources.
  11. Pipattanasomporn, M., Feroze, H., and Rahman, S. (2009). Multi-agent systems in a distributed smart grid: Design and implementation. In IEEE/PES Power Systems Conference and Exposition, Adv. Res. Inst., Virginia Tech, Arlington, VA.
  12. Rahman, S., Pipattanasomporn, M., and Teklu, Y. (2007). Intelligent distributed autonomous power systems (idaps). In IEEE Power Engineering Society General Meeting, Adv. Res. Inst. of Virginia Tech, Arlington, VA.
  13. Ramchurn, S. D., Vytelingum, P., Rogers, A., and Jennings, N. (2011). Agent-based control for decentralised demand side management in the smart grid. In The 10th International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems.
  14. Russell, B. D. and Benner, C. L. (2010). Intelligent systems for improved reliability and failure diagnosis in distribution systems. IEEE Transactions on Smart Grid.
  15. Vyatkin, V., Zhabelova, G., Ulieru, M., and McComas, D. (2010). Toward digital ecologies: Intelligent agent networks controlling interdependent infrastructures. In First IEEE International Conference on Smart Grid Communications (SmartGridComm).
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Paper Citation


in Harvard Style

Ben Meskina S., Doggaz N. and Khalgui M. (2015). An Efficient Simulator for Fault Detection and Recovery in Smart Grids - FDIRSY . In Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-758-084-0, pages 132-139. DOI: 10.5220/0005245001320139


in Bibtex Style

@conference{peccs15,
author={Syrine Ben Meskina and Narjes Doggaz and Mohamed Khalgui},
title={An Efficient Simulator for Fault Detection and Recovery in Smart Grids - FDIRSY},
booktitle={Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2015},
pages={132-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005245001320139},
isbn={978-989-758-084-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - An Efficient Simulator for Fault Detection and Recovery in Smart Grids - FDIRSY
SN - 978-989-758-084-0
AU - Ben Meskina S.
AU - Doggaz N.
AU - Khalgui M.
PY - 2015
SP - 132
EP - 139
DO - 10.5220/0005245001320139