Cascading failures in critical cyber physical systems such as power systems are rare but lead to huge social and economic implications. Timely diagnosis of faults in these systems is a challenging task due to inherent heterogeneity and scale of the system. In the past, we have successfully demonstrated a robust technique for diagnosing independent component faults using Temporal Causal Diagrams (TCD) at sub-system level. In this paper, we present a systematic approach of using the sub-system level fault models to auto-generate a systemlevel fault model that helps in diagnosing cascading failures. We show the time complexity of our model generation algorithm using industry standard Power Transmission networks. Further, we describe the updates to the existing TCD reasoner algorithms and report the TCD diagnosis results for simulated multi fault scenario on a standard power system.
How to Cite
model based diagnostics, Power systems, Cyber Physical Energy Systems, Temporal Causal Diagrams
Bi, T., Yan, Z., Wen, F., Ni, Y., Shen, C., Wu, F. F., & Yang, Q. (2002). On-line fault section estimation in power systems with radial basis function neural network. International journal of electrical power & energy systems, 24(4), 321–328.
Cardoso, G., Rolim, J. G., & Zurn, H. H. (2004, July). Application of neural-network modules to electric power system fault section estimation. IEEE Transactions on Power Delivery, 19(3), 1034-1041. doi: 10.1109/TPWRD. 2004.829911
Cardoso, G., Rolim, J. G., & Zurn, H. H. (2008, July). Identifying the primary fault section after contingencies in bulk power systems. IEEE Transactions on Power Delivery, 23(3), 1335-1342. doi: 10.1109/TPWRD. 2008.916743
Chen, W. H. (2012, April). Online fault diagnosis for power transmission networks using fuzzy digraph models. IEEE Transactions on Power Delivery, 27(2), 688-698. doi: 10.1109/TPWRD.2011.2178079
Chen, W.-H., Liu, C.-W., & Tsai, M.-S. (2001, Oct). Fast fault section estimation in distribution substations using matrix-based cause-effect networks. IEEE Transactions on Power Delivery, 16(4), 522-527. doi: 10.1109/61.956731
Chen,W. H., Tsai, S. H., & Lin, H. I. (2011, April). Fault section estimation for power networks using logic causeeffect models. IEEE Transactions on Power Delivery, 26(2), 963-971. doi: 10.1109/TPWRD.2010.2093585
Chhokra, A., Dubey, A., Mahadevan, N., & Karsai, G. (2017). Hierarchical reasoning about faults in cyber-physical energy systems using temporal causal diagrams. International Journal of Prognostics and Health Management, Submitted to. Retrieved from http://www.isis.vanderbilt.edu/sites
Di Fazio, A. R., Erseghe, T., Ghiani, E., Murroni, M., Siano, P., & Silvestro, F. (2013). Integration of renewable energy sources, energy storage systems, and electrical vehicles with smart power distribution networks. Journal of Ambient Intelligence and Humanized Computing, 4(6), 663–671.
Dugan, R. (2016). Opendss manual. Electrical Power Research Institute. Retrieved from http://sourceforge.net/apps/mediawiki/electricdss/index.php
Ferreira, V., Zanghi, R., Fortes, M., Sotelo, G., Silva, R., Souza, J., . . . Gomes, S. (2016). A survey on intelligent system application to fault diagnosis in electric power system transmission lines. Electric Power Systems Research, 136, 135–153.
Group, W. (1973, Nov). Common format for exchange of solved load flow data. IEEE Transactions on Power Apparatus and Systems, PAS-92(6), 1916-1925. doi: 10.1109/TPAS.1973.293571
Guo, W., Wei, L., Wen, F., Liao, Z., Liang, J., & Tseng, C. L. (2009, April). An on-line intelligent alarm analyzer for power systems based on temporal constraint network. In Sustainable power generation and supply, 2009. supergen ’09. international conference on (p. 1-7). doi: 10.1109/SUPERGEN.2009.5347900
Guo, W., Wen, F., Ledwich, G., Liao, Z., He, X., & Liang, J. (2010, July). An analytic model for fault diagnosis in power systems considering malfunctions of protective relays and circuit breakers. IEEE Transactions on Power Delivery, 25(3), 1393-1401. doi: 10.1109/TPWRD. 2010.2048344
Hare, J., Shi, X., Gupta, S., & Bazzi, A. (2016). Fault diagnostics in smart micro-grids: A survey. Renewable and Sustainable Energy Reviews, 60, 1114–1124.
He, Z., Chiang, H.-D., Li, C., & Zeng, Q. (2009). Faultsection estimation in power systems based on improved optimization model and binary particle swarm optimization. In Power & energy society general meeting, 2009. pes’09. ieee (pp. 1–8).
Huang, Y.-C. (2002, May). Fault section estimation in power systems using a novel decision support system. IEEE Transactions on Power Systems, 17(2), 439-444. doi: 10.1109/TPWRS.2002.1007915
Jones, L. E. (2014). Renewable energy integration: practical management of variability, uncertainty, and flexibility in power grids. Academic Press.
Jung, J., Liu, C.-C., Hong, M., Gallanti, M., & Tornielli, G. (2001, Apr). Multiple hypotheses and their credibility in on-line fault diagnosis. IEEE Transactions on Power Delivery, 16(2), 225-230. doi: 10.1109/61.915487
Krˇc´al, P., Mokrushin, L., Thiagarajan, P., & Yi, W. (2004). Timed vs. time-triggered automata. In Concur 2004-concurrency theory (pp. 340–354). Springer.
Mahadevan, N., Dubey, A., Karsai, G., Srivastava, A., & Liu, C.-C. (2014). Temporal causal diagrams for diagnosing failures in cyber-physical
systems. Annual Conference of the Prognostics and Health Management Society. Retrieved from http://www.phmsociety.org/node/1439
Mahanty, R. N., & Gupta, P. B. D. (2004, March). Application of rbf neural network to fault classification and location in transmission lines. IEE Proceedings - Generation, Transmission and Distribution, 151(2), 201-212. doi: 10.1049/ip-gtd:20040098
NERC. (2005). Evaluation of criteria, methods, and practices used for system design, planning, and analysis response to nerc blackout recommendation 13c [Computer software manual].
NERC. (2013). Transmission system planning performance requirements - nerc standard tpl-001-4 [Computer software manual].
North American Electric Reliability Corporation. (2012). 2012 state of reliability (Tech. Rep.). Retrieved from http://www.nerc.com/files/2012 sor.pdf
Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995, Sep). Diagnosability of discrete-event systems. IEEE Transactions
on Automatic Control, 40(9), 1555-1575. doi: 10.1109/9.412626
Schweitzer, E., Fleming, B., Lee, T. J., Anderson, P. M., et al. (1997). Reliability analysis of transmission protection using fault tree methods. In Proceedings of the 24th annual western protective relay conference (pp. 1–17).
Schweitzer, E. O., Kasztenny, B., Guzm´an, A., Skendzic, V., & Mynam, M. V. (2014). Speed of line protection–can we break free of phasor limitations? In 41st annual western protective relay conference, spokane, washington usa.
Sekine, Y., Akimoto, Y., Kunugi, M., Fukui, C., & Fukui, S. (1992). Fault diagnosis of power systems. Proceedings of the IEEE, 80(5), 673–683.
Sun, J., Qin, S.-Y., & Song, Y.-H. (2004, Nov). Fault diagnosis of electric power systems based on fuzzy petri nets. IEEE Transactions on Power Systems, 19(4), 2053-2059. doi: 10.1109/TPWRS.2004.836256
Thukaram, D., Khincha, H. P., & Vijaynarasimha, H. P. (2005, April). Artificial neural network and support vector machine approach for locating faults in radial distribution systems. IEEE Transactions on Power Delivery, 20(2), 710-721. doi: 10.1109/TPWRD. 2005.844307
Tripakis, S. (2002). Fault diagnosis for timed automata. In International symposium on formal techniques in realtime and fault-tolerant systems (pp. 205–221).
Venkatesh, C., & Swarup, K. S. (2012). Investigating performance of numerical distance relay with higher sampling rate. In North american power symposium (naps), 2012 (pp. 1–6).
Wen, F., & Chang, C. (1997). Probabilistic approach for fault-section estimation in power systems based on a refined genetic algorithm. In Generation, transmission and distribution, iee proceedings- (Vol. 144, pp. 160–168).
Wu, Y.-X., ning Lin, X., hong Miao, S., Liu, P., qing Wang, D., & bin Chen, D. (2005). Application of family eugenics based evolution algorithms to
electric power system fault section estimation. In Transmission and distribution conference and exhibition: Asia and pacific, 2005 ieee/pes (p. 1-5). doi: 10.1109/TDC.2005.1546813
Yongli, Z., Limin, H., & Jinling, L. (2006, April). Bayesian networks-based approach for power systems fault diagnosis. IEEE Transactions on Power Delivery, 21(2), 634-639. doi: 10.1109/TPWRD.2005.858774
Yongli, Z., Yang, Y. H., Hogg, B. W., Zhang, W. Q., & Gao, S. (1994, Feb). An expert system for power systems fault analysis. IEEE Transactions on Power Systems, 9(1), 503-509. doi: 10.1109/59.317573
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