Abstract
Power systems are naturally exposed to several fault conditions, namely short-circuits. Automatic protection and control systems are responsible for selective tripping of faulted devices and their subsequent reconnection.
An application to automatically diagnosing abnormal conditions in power distribution network substations is described in this work. It is based on a model of the power network and the associated protection and control system. Its main purpose is to assist in the analysis of protection device records, explain the observed operations and detect possible misbehavior. This application is specified and implemented using a combination of Logic Programming tools and techniques, in particular non-monotonic reasoning. The model is structured in several abstraction levels and a set of strategies and preferences is used to guide the diagnosis process.
The developed application was tested with several substation configurations, including sequences of events recorded at real installations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Böttcher, C., Dressler, O.: A framework for controlling model-based diagnosis systems with multiple actions. Ann. Math. A.I. (special issue on Model-based Diagnosis) 11 (1994)
de Kleer, J., Williams, B.: Diagnosing multiple faults. Artificial Intelligence 32(1), 97–130 (1987)
Davidson, E., McArthur, S., McDonald, J.: A toolset for applying model-based reasoning techniques to diagnostics for power system protection. IEEE Trans. Power Systems 18(2), 680–687 (2003)
Damásio, C., Nejdl, W., Pereira, L., Schroeder, M.: Model-based diagnosis preferences and strategies representation with meta-logic programming. In: Apt, K., Turini, F. (eds.) Meta-logics and Logic Prog., vol. 11, pp. 269–311. MIT Press, Cambridge (1995)
Eiter, T., Faber, W., Leone, N., Pfeifer, G.: The diagnosis frontend of the dlv system. Tech. Rep. DBAI-TR-98-20, Tech. Univ. Wien, Austria (November 1998)
Gelfond, M., Lifschitz, V.: Logic programs with classical negation. In: Proc. 7th Int. Conf. on L.P., pp. 579–597. MIT Press, Cambridge (1990)
Jorge, R.: Power network protection system diagnosis. Master Thesis (in portuguese), FCT, Univ. Nova. Lisboa (2005)
Pereira, L., Damásio, C., Alferes, J.: Diagnosis and debugging as contradiction removal. In: Proc. 2nd Int. Workshop on LP and Non-monotonic Reasoning, Lisboa, Portugal, pp. 334–348 (June 1993)
Pfau-Wagenbauer, M., Nejdl, W.: Model/heuristic-based alarm processing for power systems. Journal on AI in Eng., Design and Manuf. 7(1), 65–78 (1993)
Poole, D.: Normality and faults in logic-based diagnosis. In: Proc. of 11th IJCAI, Detroit, MI, pp. 1304–1310 (August 1989)
Sidhu, T., Cruder, O., Huff, G.: An abductive inference technique for fault diagnosis in electrical power transmission networks. IEEE Trans. Power Delivery 12(1), 515–522 (1997)
Smodels system. version 2.27 (2003), http://www.tcs.hut.fi/softare/smodels/
Struss, P.: Diagnosis as a process. In: Work. Notes 1st Int. Workshop on Model-based diagnosis, Paris (1989)
Struss, P., Dressler, O.: Physical negation: integrating fault models into the general diagnostic engine. In: Proc. 11th IJCAI, Detroit, MI, pp. 1318–1323 (August 1989)
Vale, Z., Moura, A.: An expert system with temporal reasoning for alarm processing in power system control centers. IEEE Trans. Power Systems 8(3), 1307–1313 (1993)
XSB Logic Programming system. version 2.6 (2003), http://xsb.sourceforge.net/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jorge, R.D., Damásio, C.V. (2006). Diagnosis of Power System Protection. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_70
Download citation
DOI: https://doi.org/10.1007/11779568_70
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
eBook Packages: Computer ScienceComputer Science (R0)