Vulnerability Assessment Model of Power Grid Cascading Failures Based on Fault Chain and Dynamic Fault Tree

Cyber-physical security of the smart grid obtains increasing attention. Critical transmission lines have a major impact on large-scale cascading failures in modern power grids. In this paper, we proposed a predicting model of cascading failures based on the fault chain theory and Fuzzy C-Means. The development process of cascading failures in a power grid is described based on the fault chain and dynamic fault tree theories. Loading rate, coupling relationship of sequent tripped lines and power flow variation are considered in the proposed method to predictive the fault chains comprehensively. A novel branch vulnerability assessment method is proposed based on the fault chain and dynamic fault tree theories to identify critical branches which have a significant impact on cascading failure expansion. The effectiveness of the proposed assessment model is tested based on IEEE 39-bus system.

[1]  J. B. Dugan,et al.  Sensitivity analysis of modular dynamic fault trees , 2000, Proceedings IEEE International Computer Performance and Dependability Symposium. IPDS 2000.

[2]  Hong Xu,et al.  Combining dynamic fault trees and event trees for probabilistic risk assessment , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.

[3]  I. Kamwa,et al.  Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance , 2005, IEEE Transactions on Power Systems.

[4]  Lei Shi,et al.  Dynamic fault tree analysis based fault diagnosis system of power transformer , 2012, Proceedings of the 10th World Congress on Intelligent Control and Automation.

[5]  Lingfeng Wang,et al.  Critical line identification for hypothesized multiple line attacks against power systems , 2016, 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D).

[6]  Gang Wang,et al.  An Improved OPA Model and Blackout Risk Assessment , 2009, IEEE Transactions on Power Systems.

[7]  Zhao Yang Dong,et al.  The 2015 Ukraine Blackout: Implications for False Data Injection Attacks , 2017, IEEE Transactions on Power Systems.

[8]  Zhu Fang,et al.  The Improvement of the Small-world Network Model and Its Application Research in Bulk Power System , 2006, 2006 International Conference on Power System Technology.

[9]  Paul Hines,et al.  A “Random Chemistry” Algorithm for Identifying Collections of Multiple Contingencies That Initiate Cascading Failure , 2012, IEEE Transactions on Power Systems.

[10]  Ansi Wang,et al.  Vulnerability Assessment Scheme for Power System Transmission Networks Based on the Fault Chain Theory , 2011, IEEE Transactions on Power Systems.

[11]  Chun Yang,et al.  Clustering performance of different density function weighted FCM algorithm , 2010, 2010 Sixth International Conference on Natural Computation.

[12]  Qingqing Huang,et al.  Dynamic detection of transmission line outages using Hidden Markov Models , 2015, 2015 American Control Conference (ACC).

[13]  Xiaolei Li,et al.  A novel accident model and its application to hazard analysis , 2009, 2009 Annual Reliability and Maintainability Symposium.

[14]  Farrokh Aminifar,et al.  On-line assessment of transmission line thermal rating using PMU data , 2014, 2014 Smart Grid Conference (SGC).

[15]  J. Bezdek.  A Physical Interpretation of Fuzzy ISODATA , 1993 .

[16]  Haibo He,et al.  Supplementary File : Revealing Cascading Failure Vulnerability in Power Grids using Risk-Graph , 2013 .

[17]  J. De La Ree,et al.  Analysis of hidden failures of protection schemes in large interconnected power systems , 2004, IEEE Power Engineering Society General Meeting, 2004..