Distribution power system outage diagnosis based on root cause analysis

Document Type : Article


School of Electrical and Computer Engineering, Department of Engineering, Shiraz University, Zand Avenue, Shiraz, P.O. Box 71348-51154, Iran.


This paper proposes data mining-based models to diagnose outage data in distribution power systems. In this work, outage data from a local distribution company is gathered and aligned with weather data. Then, a subset of features is selected to reduce the processing time and simplifying purposes. To increase the fairness of final models and to account for differences in misclassification cost, using a customized cost matrix is proposed. Two decision tree-based modeling algorithms are trained and tested. Results show the ability of the established models to diagnose the root cause of an outage fairly well. In addition, an ensemble of the decision tree-based models is built, which outperforms the other two models in almost all cases. Finally, applications of such models in decreasing outage duration and improving the reliability of the power distribution network are discussed.


1. Zheng, K., Chen, Q., Wang, Y., et al. "A novel combined data-driven approach for electricity theft detection", IEEE Trans. Ind. Informatics, 15(3), pp. 1809-1819 (2018).
2. Jokar, P., Arianpoo, N., and Leung, V.C.M. "Electricity theft detection in AMI using customers consumption patterns", IEEE Trans. Smart Grid, 7(1), pp. 216-226 (2016).
3. Singh, S.K., Bose, R., and Joshi, A. "PCA based electricity theft detection in advanced metering infrastructure", 7th Int. Conf. Pow. Syst. (ICPS), pp. 441- 445 (2017).
4. Venkatesh, A., Cokkinides, G., and Meliopoulos, A.P.S. "3D-visualization of power system data using triangulation and subdivision techniques", Proc. 42nd Annu. Hawaii Int. Conf. Syst. Sci. HICSS, pp. 1-8 (2009).
5. McMorran, A.W., Ault, G.W., and McDonald, J.R. "Solving data integration challenges for web-based geographical power system data visualization using CIM",IEEE Power Energy Soc. 2008 Gen.  Meet. Convers. Deliv. Electr. Energy 21st Century, PES (2008).
6. Bank, J.N., Omitaomu, O.A., Fernandez, S.J., et al. "Visualization and classification of power system frequency data streams", ICDM Work. 2009 - IEEE Int. Conf. Data Min., pp. 650-655 (2009).
7. Brown, R.E., Electric Power Distribution Reliability, 2nd Edn., CRC press (2008).
8. Schroder, T. and Kuckshinrichs, W. "Value of lost load: An efficient economic indicator for power supply security? A literature review", Front. Energy Res., 3, p. 55 (2015).
9. Eskandarpour, R. and Khodaei, A. "Machine learning based power grid outage prediction in response to extreme events", IEEE Trans. Power Syst., 32(4), pp. 3315-3316 (2017).
10. Wanik, D.W., Anagnostou, E.N., Hartman, B.M., et al. "Storm outage modeling for an electric distribution network in Northeastern USA", Nat. Hazards, 79(2), pp. 1359-1384 (2015).
11. Doostan, M. and Chowdhury, B.H. "Power distribution system fault cause analysis by using association rule mining", Electr. Power Syst. Res., 152, pp. 140- 147 (2017).
12. Cai, Y. and Chow, M.Y. "Cause-effect modeling and spatial-temporal simulation of power distribution fault events", IEEE Trans. Power Syst., 26(2), pp. 794-801 (2011).
13. Chow, M. and Taylor, L.S. "Analysis and prevention of animal-caused faults in power distribution systems", IEEE Trans. Power Deliv., 10(2), pp. 995-1001 (1995).
14. Warlyani, P., Jain, A., Thoke, A.S., et al. "Fault classification and faulty section identification in teed transmission circuits using ANN", Int. J. Comput. Electr. Eng., 3(6), pp. 807-811 (2012).
15. Xu, L., Chow, M.-Y., and Taylor, L.S. "Power distribution fault cause identification with imbalanced data using the data mining-based fuzzy classification E-Algorithm", IEEE Trans. Power Syst., 22(1), pp. 164-171 (2007).
16. Doostan, M. and Chowdhury, B.H. "Power distribution system equipment failure identification using machine learning algorithms", IEEE Pow. Energy Soc. Gen. Meet., pp. 1-5 (2018).
17. Doostan, M., Sohrabi, R., and Chowdhury, B. "A data-driven approach for predicting vegetation-related outages in power distribution systems", arXiv Prepr. arXiv1807.06180, pp. 1-11 (2018).
18. Breiman, L. "Random forests", Mach. Learn., 45(1), pp. 5-32 (2001).
19. Quinlan, J.R. "C5.0 data mining tool", RuleQuest Research, 63, p. 64 (1997).
20. Quinlan, J.R. "C 4.5: Programs for machine learning", Morgan Kaufmann Ser. Mach. Learn. San Mateo, CA Morgan Kaufmann (1993).
21. Hinton, G. and Van Camp, D. "Keeping neural networks simple by minimizing the description length of the weights", Proc. 6th Ann. ACM Conf. on Computational Learning Theory (1993).
22. Raspisaniye Pogodi Ltd, Reliable Prognosis. Weather and Climate Change, Available Online at 2019: https://rp5.ru/.
23. Liu, H. and Motoda, H. "Feature selection for knowledge discovery and data mining", 454, Springer Science & Business Media (2012).
24. Guyon, I. and Elisseeff, A. "An introduction to variable and feature selection", J. Mach. Learn. Res., 3(Mar), pp. 1157-1182 (2003).