A Cascading Failure Prediction Method in Power System Based on Multi-Agent and Hybrid Genetic Algorithm

Juan Li

doi:10.4028/www.scientific.net/AMM.494-495.1598

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 494-495A Cascading Failure Prediction Method in Power...

A Cascading Failure Prediction Method in Power System Based on Multi-Agent and Hybrid Genetic Algorithm

Abstract:

To effectively predict cascading failure in power system, a cascading failure prediction method in power system based on multi-agent and hybrid genetic algorithm is constructed. A cascading failure prediction procedure in power system was established by multi-agent and hybrid genetic algorithm to investigate the emergent behaviors of cascading failures and to further study the prediction and defense of cascading failures. Finally, the cascading failure prediction simulation system of power system based on this method was demonstrated and validated by Flexsim software. The result showed that the proposed method was available, and can provided guidance for avoiding and predict cascading failure in power system, and support for stable performance in power system.

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Periodical:

Applied Mechanics and Materials (Volumes 494-495)

Pages:

1598-1601

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.494-495.1598

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Online since:

February 2014

Authors:

Juan Li*

Keywords:

Cascading Failure, Hybrid Genetic Algorithm, Multi-Agent (MA), Power System, Prediction

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* - Corresponding Author

References

[1] L. Chang and Z. Wu, Performance and reliability of electrical power grids under cascading failures[J]. International Journal of ElectricalPower & Energy Systems. 2011 (33) 1410-1419.

DOI: 10.1016/j.ijepes.2011.06.021

Google Scholar

[2] J. Wang and L. Rong, Robustness of the western United States power grid under edge attack strategies due to cascading failures[J]. Safety Science. 2011 (49) 807-812.

DOI: 10.1016/j.ssci.2010.10.003

Google Scholar

[3] Z.J. Bao, Y.J. Cao, G.Z. Wang, and L.J. Ding, Analysis of cascading failure in electric grid based on power flow entropy[J]. Physics Letters A. 2009 (373) 3032-3040.

DOI: 10.1016/j.physleta.2009.06.058

Google Scholar

[4] L. Dueñas-Osorio and S. M. Vemuru, Cascading failures in complex infrastructure systems[J]. Structure Safety. 2009 (31) 157-167.

DOI: 10.1016/j.strusafe.2008.06.007

Google Scholar

[5] J. Chen, J. S. Thorp, and I. Dobson, Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model[J]. International Journal of ElectricalPower & Energy Systems. 2005 (27) 318-326.

DOI: 10.1016/j.ijepes.2004.12.003

Google Scholar

[6] H. Song and M. Kezunovic, A new analysis method for early detection and prevention of cascading events[J]. Electric Power Systems Research. 2007 (77) 1132-1142.

DOI: 10.1016/j.epsr.2006.09.010

Google Scholar

[7] A. H. Gharehgozli, R. Tavakkoli-Moghaddam, and N. Zaerpour, A fuzzy-mixed-integer goal programming model for a parallel-machine scheduling problem with sequence-dependent setup times and release dates[J]. Robotics and Computer-integrated Manufacturing, 2009 (25) 853-859.

DOI: 10.1016/j.rcim.2008.12.005

Google Scholar

[8] K. Chen and C. Chen, Applying multi-agent technique in multi-section flexible power system system[J]. Expert Systems with Applications. 2010 (37) 7310-7318.

DOI: 10.1016/j.eswa.2010.04.024

Google Scholar

[9] L. Wang and S. Lin, A multi-agent based agile power system planning and control system[J]. Computers in Industry. 2009 (57) 620-640.

DOI: 10.1016/j.cie.2009.05.015

Google Scholar

[10] M. G. Sahab, A. F. Ashour, and V. V. Toropov, A hybrid genetic algorithm for reinforced concrete flat slab buildings[J]. Automation in Construction, 2005 (83) 551-559.

DOI: 10.1016/j.compstruc.2004.10.013

Google Scholar

[11] K. Deep, and K. N. Das, Quadratic approximation based hybrid genetic algorithm for function optimization[J]. Applied Mathematics and Computation. 2008 (203) 86-98.

DOI: 10.1016/j.amc.2008.04.021

Google Scholar

[12] HaIjunkoskjl, Grossmann I E, A decomposition approach for the scheduling of a steel plant production[J]. Computers and Chemical Engineering, 2001 (25) 1647-1660.

DOI: 10.1016/s0098-1354(01)00729-3

Google Scholar