Temporal Causal Diagrams for Diagnosing Failures in Cyber-Physical Systems
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Abstract
Resilient and reliable operation of cyber physical systems of societal importance such as Smart Electric Grids is one of the top national priorities. Due to their critical nature, these systems are equipped with fast-acting, local protection mechanisms. However, commonly misguided protection actions together with system dynamics can lead to un-intentional cascading effects. This paper describes the ongoing work using Temporal Causal Diagrams (TCD), a refinement of the Timed Failure Propagation Graphs (TFPG), to diagnose problems associated with the power transmission lines protected by a combination of relays and breakers.
The TCD models represent the faults and their propagation as TFPG, the nominal and faulty behavior of components (including local, discrete controllers and protection devices) as Timed Discrete Event Systems (TDES), and capture the cumulative and cascading effects of these interactions. The TCD diagnosis engine includes an extended TFPG-like reasoner which in addition to observing the alarms and mode changes (as the TFPG), monitors the event traces (that correspond to the behavioral aspects of the model) to generate hypotheses that consistently explain all the observations. In this paper, we show the results of applying the TCD to a segment of a power transmission system that is protected by distance relays and breakers.
How to Cite
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fault diagnosis, Power systems, temporal causal networks
Abdelwahed, S., Karsai, G., & Biswas, G. (2005). A consistency-based robust diagnosis approach for temporal causal systems. In The 16th international workshop on principles of diagnosis. Pacific Grove, CA.
Abdelwahed, S., Karsai, G., Mahadevan, N., & Ofsthun, S. (2009). Practical Implementation of Diagnosis Systems Using Timed Failure Propagation Graph Models. Instrumentation and Measurement, IEEE Transactions on, 58(2), 240–247.
Bastos, J. L., Zhang, Y., Srivastava, A. K., & Schulz, N. N. (2007). A design paradigm for integrated protection of shipboard power systems. In Proceedings of the 2007 summer computer simulation conference (pp. 3:1–3:10). San Diego, CA, USA: Society for Computer Simulation International.
Chen, W., Liu, C., & Tsai, M. (2000). On-line fault diagnosis of distribution substations using hybrid cause-effect network and fuzzy rule-based method. Power Delivery, IEEE Transactions on, 15(2), 710–717.
Chen, W.-H., Liu, C.-W., & Tsai, M.-S. (2000, Apr). On-line fault diagnosis of distribution substations using hybrid cause-effect network and fuzzy rule-based method. Power Delivery, IEEE Transactions on, 15(2), 710-717. doi: 10.1109/61.853009
Console, L., & Torasso, P. (1991). On the co-operation between abductive and temporal reasoning in medical diagnosis. Artificial Intelligence in Medicine, 3(6), 291- 311.
Coster, E., Myrzik, J., Kruimer, B., & Kling, W. (2011, Jan- uary). Integration issues of distributed generation in distribution grids. Proceedings of the IEEE, 99(1), 28 -39. doi: 10.1109/JPROC.2010.2052776
Daigle, M., Roychoudhury, I., Biswas, G., Koutsoukos, X., Patterson-Hine, A., & Poll, S. (2010). A Comprehensive Diagnosis Methodology for Complex Hybrid Systems: A Case Study on Spacecraft Power Distribution Systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 40(5), 917–931.
Fukui, C., & Kawakami, J. (1986, Oct). An expert system for fault section estimation using information from protective relays and circuit breakers. Power Delivery, IEEE Transactions on, 1(4), 83-90. doi: 10.1109/TP- WRD.1986.4308033
Garrity, T. (2008). Getting smart. Power and Energy Magazine, IEEE, 6(2), 38–45.
Guo, W., Wen, F., Ledwich, G., Liao, Z., He, X., & Liang, J. (2010). An Analytic Model for Fault Diagnosis in Power Systems Considering Malfunctions of Protective Relays and Circuit Breakers. Power Delivery, IEEE Transactions on, 25(3), 1393–1401.
Ilic, M., Allen, H., Chapman, W., King, C., Lang, J. H., & Litvinov, E. (2005, Nov). Preventing future blackouts by means of enhanced electric power systems control: From complexity to order. Proceedings of the IEEE, 93(11), 1920-1941. doi: 10.1109/JPROC.2005.857496
Karsai, G., Sztipanovits, J., Padalkar, S., & Biegl, C. (1992). Model based intelligent process control for cogenerator plants. Journal of Parallel and Distributed Systems, 15, 90–103.
Lee, S., Choi, M., Kang, S., Jin, B., Lee, D., Ahn, B., ... Wee, S. (2004). An intelligent and efficient fault location and diagnosis scheme for radial distribution systems. Power Delivery, IEEE Transactions on, 19(2), 524–532.
Lin, X., Ke, S., Li, Z., Weng, H., & Han, X. (2010). A Fault Diagnosis Method of Power Systems Based on Improved Objective Function and Genetic Algorithm- Tabu Search. Power Delivery, IEEE Transactions on, 25(3), 1268–1274.
Mele ́ndez, J., Macaya, D., Colomer, J., Llanos, D., Gervas, P., & Gupta, K. (2004). Symptom based representation for dynamic systems diagnosis. Application to Electrical Power Distribution. In Proceedings of the eccbr workshops. edited by p. gervas and km gupta. university of madrid, madrid (pp. 311–327).
Mengshoel, O., Chavira, M., Cascio, K., Poll, S., Darwiche, A., & Uckun, S. (2010). Probabilistic model-based diagnosis: An electrical power system case study. Sys- tems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 40(5), 874–885.
Miao, H., Sforna, M., & Liu, C.-C. (1996, Aug). A new logic- based alarm analyzer for online operational environment. Power Systems, IEEE Transactions on, 11(3), 1600-1606. doi: 10.1109/59.535703
Misra, A. (1994). Sensor-based diagnosis of dynamical systems. Unpublished doctoral dissertation, Vanderbilt University.
Misra, A., Sztipanovits, J., & Carnes, J. (1994). Robust diagnostics: Structural redundancy approach. In Spie’s symposium on intelligent systems.
Mosterman, P. J., & Biswas, G. (1999). Diagnosis of continuous valued systems in transient operating regions. IEEE Trans. on Systems, Man and Cybernetics, 29(6), 554-565.
North American Electric Reliability Corporation. (2012). 2012 state of reliability (Tech. Rep.). Retrieved from http://www.nerc.com/files/2012 sor.pdf
Padalkar, S., Sztipanovits, J., Karsai, G., Miyasaka, N., & Okuda, K. C. (1991). Real-time fault diagnostics. IEEE Expert, 6(3), 75–85.
Poll, S., Patterson-Hine, A., Camisa, J., Garcia, D., Hall, D., Lee, C., . . . others (2007). Advanced diagnostics and prognostics testbed. In Proceedings of the 18th international workshop on principles of diagnosis (dx-07) (pp. 178–185).
Pourbeik, P., Kundur, P., & Taylor, C. (2006). The anatomy of a power grid blackout-root causes and dynamics of recent major blackouts. Power and Energy Magazine, IEEE, 4(5), 22–29.
Ren, H., Mi, Z., Zhao, H., & Yang, Q. (2005). Fault diagnosis for substation automation based on Petri nets and coding theory. In Power engineering society general meeting, 2004. IEEE (pp. 1038–1042).
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1996, March). Failure diagnosis using discrete-event models. IEEE Transactions On Control System Technology, 4(2), 105–124.
Sekine, Y., Akimoto, Y., Kunugi, M., Fukui, C., & Fukui, S. (2002). Fault diagnosis of power systems. Proceedings of the IEEE, 80(5), 673–683.
Sun, J., Qin, S., & Song, Y. (2004). Fault diagnosis of elec- tric power systems based on fuzzy Petri nets. Power Systems, IEEE Transactions on, 19(4), 2053–2059.
Taha, W., Brauner, P., Zeng, Y., Cartwright, R., Gaspes, V., Ames, A., & Chapoutot, A. (2012, June). A core language for executable models of cyber-physical systems (preliminary report). In Distributed computing systems workshops (icdcsw), 2012 32nd international conference on (p. 303-308). doi: 10.1109/ICDCSW.2012.72
Tholomier, D., Richards, S., & Apostolov, A. (2007, Aug). Advanced distance protection applications fot dynamic loading and out-of step condition. In Bulk power system dynamics and control - vii. revitalizing operational reliability, 2007 irep symposium (p. 1-8). doi: 10.1109/IREP.2007.4410560
Yang, C., Okamoto, H., Yokoyama, A., & Sekine, Y. (1992). Expert system for fault section estimation of power systems using time-sequence information. International Journal of Electrical Power & Energy Systems, 14(2- 3), 225–232.
Yongli, Z., Limin, H., & Jinling, L. (2006). Bayesian networks-based approach for power systems fault diagnosis. Power Delivery, IEEE Transactions on, 21(2), 634–639.
Zhang, Y., Ilic, M., & Tonguz, O. (2011, January). Miti- gating blackouts via smart relays: A machine learning approach. Proceedings of the IEEE, 99(1), 94 -118. doi: 10.1109/JPROC.2010.2072970
Zhou, G. (1993). A neural network approach to fault diagnosis for power systems. In Tencon’93. proceedings. com- puter, communication, control and power engineering. 1993 IEEE region 10 conference on (pp. 885–888).
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