Abstract
This paper addresses power line topology change detection by using only measurement data. As Phasor Measurement Units (PMUs) become widely deployed, power system monitoring and real-time analysis can take advantage of the large amount of data provided by PMUs and leverage the advances in big data analytics. In this paper, we develop practical analytics that are not tightly coupled with the power flow analysis and state estimation, as these tasks require detailed and accurate information about the power system. We focus on power line outage identification, and use a machine learning framework to locate the outage(s). The same framework is used for both single line outage identification and multiple line outage identification. We first compute the features that are essential to capture the dynamic characteristics of the power system when the topology change happens, transform the time-domain data to frequency-domain, and then train the algorithms for the prediction of line outage based on frequency domain features. The proposed method uses only voltage phasor angles obtained by continuous monitoring of buses. The proposed method is tested by simulated PMU data from PSAT [1], and the prediction accuracy is comparable to the previous work that involves solving power flow equations or state estimation equations.
M. Cheng is supported in part by National Science Foundation of USA.
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Notes
- 1.
Observed means PMUs are installed on the buses at the two end points of the line.
References
Milano, F., Vanfretti, L., Morataya, J.C.: An open source power system virtual laboratory: the PSAT case and experience. IEEE Trans. Educ. 51(1), 17–23 (2008)
Andersson, G., et al.: Causes of the 2003 major grid blackouts in north America and Europe, and recommended means to improve system dynamic performance. IEEE Trans. Power Syst. 20(4), 1922–1928 (2005)
US Canada Power System Outage Task - Force: Final report on the august 14, 2003 blackout in the united states and canada: causes and recommendations, 5 April 2004
Amin, M.: Three questions about physical grid security. In: IEEE Smart Grid, March 2014
Zhu, H., Giannakis, G.B.: Sparse overcomplete representations for efficient identification of power line outages. IEEE Trans. Power Syst. 27(4), 2215–2224 (2012)
Wu, W.B., Cheng, M.X., Gou, B.: A hypothesis testing approach for topology error detection in power grids. IEEE Internet Things J. 3(6), 979–985 (2016)
Zhao, Y., Chen, J., Goldsmith, A., Poor, H.V.: Identification of outages in power systems with uncertain states and optimal sensor locations. IEEE J. Sel. Top. Sign. Proces. 8(6), 1140–1153 (2014)
Tate, J.E., Overbye, T.J.: Line outage detection using phasor angle measurements. IEEE Trans. Power Syst. 23(4), 1644–1652 (2008)
Tate, J.E., Overbye, T.J.: Double line outage detection using phasor angle measurements. In: 2009 IEEE Power Energy Society General Meeting, pp. 1–5, July 2009
Garcia, M., Catanach, T., Wiel, S.V., Bent, R., Lawrence, E.: Line outage localization using phasor measurement data in transient state. IEEE Trans. Power Syst. 31(4), 3019–3027 (2016)
Gorinevsky, D., Boyd, S., Poll, S.: Estimation of faults in DC electrical power system. In: 2009 American Control Conference, pp. 4334–4339, June 2009
Emami, R., Abur, A.: Tracking changes in the external network model. In: North American Power Symposium 2010, pp. 1–6, September 2010
Cheng, M.X., Ling, Y., Wu, W.B.: In-band wormhole detection in wireless ad hoc networks using change point detection method. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6, May 2016
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
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He, J., Cheng, M.X., Fang, Y., Crow, M.L. (2019). A Machine Learning Approach for Line Outage Identification in Power Systems. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2018. Lecture Notes in Computer Science(), vol 11331. Springer, Cham. https://doi.org/10.1007/978-3-030-13709-0_41
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