Probability models for estimating the probabilities of cascading outages in high-voltage transmission network

This paper discusses a number of probability models for multiple transmission line outages in power systems, including generalized Poisson model, negative binomial model, and exponentially accelerated model. These models are applied to the multiple transmission outage data for a 20-year period for North America. The probabilities of the propagation of transmission cascading outage are calculated. These probability magnitudes can serve as indexes for long-term planning and can also be used in short-term operational defense to such events. Results from our research show that all three models apparently explain the occurrence probability of higher order outages very well. However, the exponentially accelerated model fits the observed data and predicts the acceleration trends best. Strict chi-squared fitness tests were done to compare the fitness among these three models, and the test results are consistent with what we observe

[1]  H Lum,et al.  Modeling vehicle accidents and highway geometric design relationships. , 1993, Accident; analysis and prevention.

[2]  Ian Dobson,et al.  A branching process approximation to cascading load-dependent system failure , 2004, 37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the.

[3]  A.G. Phadke,et al.  Hidden failures in protection systems and their impact on wide-area disturbances , 2001, 2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194).

[4]  Lamine Mili,et al.  Risk assessment of catastrophic failures in electric power systems , 2004, Int. J. Crit. Infrastructures.

[5]  M. G. Lauby,et al.  An IEEE survey of US and Canadian overhead transmission outages at 230 kV and above , 1994 .

[6]  A. W. Kemp,et al.  Generalized Poisson Distributions: Properties and Applications. , 1992 .

[7]  Qiming Chen,et al.  The probability, identification, and prevention of rare events in power systems , 2004 .

[8]  Ian Dobson,et al.  Branching Process Models for the Exponentially Increasing Portions of Cascading Failure Blackouts , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[9]  Vladimiro Miranda,et al.  Why risk analysis outperforms probabilistic choice as the effective decision support paradigm for power system planning , 1998 .

[10]  Jane M. Booker,et al.  Point Process Models With Applications to Safety and Reliability , 1990 .

[11]  W. Gallus,et al.  A Bayesian Approach for Short-Term Transmission Line Thermal Overload Risk Assessment , 2002, IEEE Power Engineering Review.

[12]  J. McCalley,et al.  A cluster distribution as a model for estimating high-order event probabilities in power systems , 2005, 2004 International Conference on Probabilistic Methods Applied to Power Systems.

[13]  V. Vittal,et al.  Online Risk-Based Security Assessment , 2002, IEEE Power Engineering Review.

[14]  James D. McCalley,et al.  Dynamic decision-event trees for rapid response to unfolding events in bulk transmission systems , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[15]  James S. Thorp,et al.  Expose hidden failures to prevent cascading outages [in power systems] , 1996 .

[16]  Ian Dobson,et al.  Evidence for self-organized criticality in a time series of electric power system blackouts , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[17]  Ian Dobson,et al.  An Estimator of Propagation of Cascading Failure , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[18]  J.D. McCalley,et al.  Identifying high risk N-k contingencies for online security assessment , 2005, IEEE Transactions on Power Systems.