Hierarchical Reasoning about Faults in Cyber-Physical Energy Systems using Temporal Causal Diagrams

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Nov 19, 2020
Ajay D Chhokra Nagabhushan Mahadevan Abhishek Dubey Saqib Hasan Daniel Balasubramanian Gabor Karsai

Abstract

fault management systems that observe the state of the system, decide if there is an anomaly and then take automated actions to isolate faults. For example, in electrical networks relays and breaks isolate faults in order to arrest failure propagation and protect the healthy parts of the system. However, due to the limited situational awareness and hidden failures the protection devices themselves, through their operation (or mis-operation) may cause overloading and the disconnection of parts of an otherwise healthy system. Additionally, often there can be faults in the management system itself leading to situations where it is difficult to isolate failures. Our work presented in this paper is geared towards solution of this problem by describing the formalism of Temporal Causal Diagrams (TCD-s) that augment the failure models for the physical systems with discrete time models of protection elements, accounting for the complex interactions between the protection devices and the physical plants. We use the case study of the standard Western System Coordinating Council (WSCC) 9 bus system to describe four different fault scenarios and illustrate how our approach can help isolate these failures. Though, we use power networks as exemplars in this paper our approach can be applied to other distributed cyberphysical systems, for example water networks.

Abstract 37 | PDF Downloads 19

##plugins.themes.bootstrap3.article.details##

Keywords

Cyber Physical Energy Systems, Temporal Causal Diagrams, System Diagnosis, Power Transmission Networks

References
Abdelwahed, S., & Karsai, G. (2006, Sept). Notions of diagnosability for timed failure propagation graphs. In Autotestcon, 2006 ieee (p. 643-648). doi: 10.1109/AUTEST.2006.283740
Abdelwahed, S.,&Karsai, G. (2007). Failure prognosis using timed failure propagation graphs. Electrical Engineering.
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.
Blanke, M., Kinnaert, M., Lunze, J., Staroswiecki, M., & Schr¨oder, J. (2006). Diagnosis and fault-tolerant control (Vol. 691). Springer.
Boem, F., Ferrari, R. M., Parisini, T., & Polycarpou, M. M. (2013). Distributed fault diagnosis for continuous-time nonlinear systems: The input–output case. Annual Reviews in Control, 37(1), 163–169.
Bouamama, B. O., Biswas, G., Loureiro, R., & Merzouki, R. (2014). Graphical methods for diagnosis of dynamic systems: Review. Annual Reviews in Control, 38(2), 199 - 219. Retrieved from
http://www.sciencedirect.com/science/article/pii/S1367578814000388 doi: https://doi.org/10.1016/j.arcontrol.2014.09.004
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 cause-effect models. IEEE Transactions on Power Delivery, 26(2), 963- 971. doi: 10.1109/TPWRD.2010.2093585
Daigle, M. J., Koutsoukos, X. D., & Biswas, G. (2007). Distributed diagnosis in formations of mobile robots. IEEE Transactions on Robotics, 23(2), 353–369.
Dubey, A., Karsai, G., & Mahadevan, N. (2011). Modelbased software health management for real-time systems. In Aerospace conference, 2011 ieee (pp. 1–18).
Dugan, R. (2016). Opendss manual. Electrical Power Research Institute. Retrieved from http://sourceforge.net/apps/mediawiki/electricdss/index.php
Ferrari, R. M., Parisini, T., & Polycarpou, M. M. (2012). Distributed fault detection and isolation of large-scale discretetime nonlinear systems: An adaptive approximation approach. IEEE Transactions on Automatic Control, 57(2), 275–290.
Ferreira, V., Zanghi, R., Fortes, M., Sotelo, G., Silva, R., Souza, J., . . . Gomes Jr, S. (2016). A survey on intelligent system application to fault diagnosis in electric power system transmission lines. Electric Power Systems Research, 136, 135–153.
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
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
Isermann, R. (2006). Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media.
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
Khalili, M., & Zhang, X. (2014, Dec). Distributed fault detection in interconnected nonlinear uncertain systems. In 53rd ieee conference on decision and control (p. 6548-6553). doi: 10.1109/CDC.2014.7040416
Krˇc´al, P., Mokrushin, L., Thiagarajan, P., & Yi, W. (2004). Timed vs. time-triggered automata. In Concur 2004- concurrency theory (pp. 340–354). Springer.
Kundur, P., Balu, N., & Lauby, M. (1994). Power system stability and control. McGraw-Hill. Retrieved from https://books.google.com/books?id=2cbvyf8Ly4AC
Mahadevan, N., Dubey, A., & Karsai, G. (2011). Application of software health management techniques. In Proceedings of the 6th international symposium on software engineering for adaptive and self-managing systems (pp. 1–10). New York, NY, USA: ACM. Retrieved from http://doi.acm.org/10.1145/1988008.1988010 doi:10.1145/1988008.1988010
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
North American Electric Reliability Corporation. (2012). 2012 state of reliability (Tech. Rep.). Retrieved from http://www.nerc.com/files/2012 sor.pdf
Padalkar, S., Karsai, G., Biegl, C., Sztipanovits, J., Okuda, K., & Miyasaka, N. (1991, June). Real-time fault diagnostics. IEEE Expert, 6(3), 75-85. doi: 10.1109/64.87689
Reppa, V., Polycarpou, M. M., & Panayiotou, C. G. (2013). Multiple sensor fault detection and isolation for large-scale interconnected nonlinear systems. In Control conference (ecc), 2013 european (pp. 1952–1957).
Reppa, V., Polycarpou, M. M., & Panayiotou, C. G. (2015a). Decentralized isolation of multiple sensor faults in largescale interconnected nonlinear systems. IEEE Transactions on Automatic Control, 60(6), 1582–1596.
Reppa, V., Polycarpou, M. M., & Panayiotou, C. G. (2015b, March). Distributed sensor fault diagnosis for a network of interconnected cyberphysical systems. IEEE Transactions on Control of Network Systems, 2(1), 11-23. doi: 10.1109/TCNS.2014.2367362
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.
Shames, I., Teixeira, A. M., Sandberg, H., & Johansson, K. H. (2011). Distributed fault detection for interconnected second-order systems. Automatica, 47(12), 2757–2764. Simscape power systems: For use with matlab;[user’s guide]. (2017). MathWorks.
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 real-time and fault-tolerant systems (pp. 205–221).
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
Yan, X.-G., & Edwards, C. (2008). Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer. International Journal of control, 81(4), 591–606.
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
Zhang, Q., & Zhang, X. (2013a). Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems. Annual Reviews in Control, 37(1), 170–179.
Zhang, Q., & Zhang, X. (2013b). Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems. Annual Reviews in Control, 37(1), 170–179.
Section
Technical Papers