Risk Informed Design Refinement of a Power System Protection Scheme

To prevent wide-area disturbances, and enhance power system reliability, various forms of system protection scheme (SPS) have been designed and implemented by utilities. One of the main concerns in SPS deployment is to ensure that the system will fit with the reliability requirement specification in terms of dependability, and security. After major changes in system operating practices, or physical changes of the protected system, a refinement of the SPS decision process may be required. This paper uses a currently operational SPS as an example to demonstrate a refinement study. By incorporating an interval theory, a risk reduction worth importance concept, and a probabilistic risk-based index, the proposed procedure conducts parameter uncertainty analysis, identifies critical factors in the reliability model, performs risk assessments, and determines a better option for the refinement of the studied SPS decision process logic module.

[1]  J. Zamanali Probabilistic-risk-assessment applications in the nuclear-power industry , 1998 .

[2]  James D. McCalley,et al.  Reliability of special protection systems , 1999 .

[3]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[4]  Clifton A. Ericson,et al.  Fault tree and Markov analysis applied to various design complexities , 2000 .

[5]  G. Maggio Space Shuttle probabilistic risk assessment: methodology and application , 1996, Proceedings of 1996 Annual Reliability and Maintainability Symposium.

[6]  James W. Hall,et al.  Uncertain inference using interval probability theory , 1998, Int. J. Approx. Reason..

[7]  H. Christopher Frey,et al.  Evaluation of Selected Sensitivity Analysis Methods Based Upon Applications to Two Food Safety Process Risk Models , 2003 .

[8]  Eldon Hansen,et al.  Global optimization using interval analysis , 1992, Pure and applied mathematics.

[9]  Ron S. Kenett,et al.  Statistics for Business and Economics. , 1988 .

[10]  Vijay Vittal,et al.  A risk-based security index for determining operating limits in stability-limited electric power systems , 1997 .

[11]  G. Apostolakis Bayesian Methods in Risk Assessment , 1981 .

[12]  R.C. Wilcox,et al.  Uncertainty modeling of data and uncertainty propagation for risk studies , 2003, Fourth International Symposium on Uncertainty Modeling and Analysis, 2003. ISUMA 2003..

[13]  W. J. Galyean,et al.  Case study: reliability of the INEL-Site Power System , 1989 .

[14]  P. M. Anderson,et al.  Industry experience with special protection schemes , 1996 .

[15]  Paul M. Anderson Power System Protection , 1998 .

[16]  C.N. Lu,et al.  Reliability evaluation of a Taipower system protection scheme , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[17]  V. Vittal,et al.  Risk Assessment for Special Protection Systems , 2002, IEEE Power Engineering Review.

[18]  Roy Billinton,et al.  A comparison of Monte Carlo simulation techniques for composite power system reliability assessment , 1995, IEEE WESCANEX 95. Communications, Power, and Computing. Conference Proceedings.