Abstract
Traditional fault diagnosis methods based on relay protection are no longer suitable with multiple distributed generators in Smart Grid. In order to improve the accuracy and rapidity of fault diagnosis with DG interconnected, a novel hybrid method of intuitionistic uncertainty rough sets and BP neural network was introduced. Firstly, based on data pretreatment, the original fault diagnosis samples were discretized by the hybrid clustering method. Then, the decision attribute was reduced to delete redundant information for obtaining the minimum fault feature subset. In the course of identifying fault diagnosis through BP neural network, some output results were modified by using the inference capability of expert system. The worked example for Xigaze power system in China’s Tibet shows the effectiveness of the method and the fault identification rate is improved by 30%.
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References
Brown, R.E.: Impact of Smart Grid on distribution system design. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 51–55 (July 2008)
Momoh, J.A.: Smart grid design for efficient and flexible power networks operation and control. In: Proceedings of IEEE/PES Power Systems Conference and Exposition, pp. 30–37 (2009)
Olken, M.: A Smart Grid Partnership. IEEE Power and Energy Magazine 9(4), 4–6 (2011)
Zhai, M.Y.: Transmission characteristics of low-voltage distribution networks in China under the Smart Grids environment. IEEE Trans. on Power Delivery 26(1), 173–180 (2011)
Walling, R.A., Saint, R., Dugan, R.C., Burke, J., Kojovic, L.A.: Summary of distributed resources impact on power delivery systems. IEEE Trans. on Power Delivery 23(3), 1636–1644 (2008)
IEEE Std. 929-2000. IEEE recommended practice for utility interface of photovoltaic (PV) system. IEEE P, Iscataway (2000)
IEEE Std.1547. IEEE standard for interconnecting distributed resources with electric power systems. IEEE P, Iscataway (2003)
Huang, G., Liang, D., Hu, C., Ren, J.: An algorithm for clustering heterogeneous data streams with uncertainty. In: Proceedings of Machine Learning and Cybernetics (ICMLC) Conference, pp. 2059–2064 (2010)
Frank, H.F., Leung, H.K., Ling, S.H.: Tuning of the structure and parameters of a neural network using an improved genetic algorithm. IEEE Trans. on Neural Network 14(1), 79–88 (2003)
Tai, J., Chou, J., Liu, T.: Tuning the structure and parameters of a neural network by using hybrid taguchi genetic algorithm. IEEE Trans. on Neural Network 17(1), 69–80 (2006)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Science 11(5), 341–356 (1982)
Lin, L., Liu, Z., Duan, X.: Network intrusion detection by a hybrid method of rough set and RBF neural network. In: Proceedings of 2nd International Conference on Education Technology and Computer, pp. 317–320 (2010)
Pang, Q., Liu, X., Zhang, M.: Improved neural network based on rough set and application in fault line detection for distribution network. In: Proceedings of 3rd International Congress on Image and Signal Processing (CISP), pp. 3784–3788 (2010)
Fan, L., Lei, Y.: An attribute reduction algorithm for intuitionistic fuzzy rough sets. Computer Engineering and Science 30(7), 79–81 (2008)
Gao, F., Iravani, M.R.: A control strategy for a distributed generation unit in grid-connected and autonomous modes of operation. IEEE Trans. on Power Delivery 23(2), 850–859 (2008)
Menon, V., Nehrir, M.H.: A hybrid islanding detection technique using voltage unbalance and frequency set point. IEEE Trans. on Power Systems 22(1), 442–448 (2007)
Jayaweera, D., Galloway, S., Burt, G., McDonald, J.R.: A sampling approach for intentional islanding of distributed generation. IEEE Trans. on Power Systems 22(2), 514–521 (2007)
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Sun, Q., Li, Z., Liu, Z., Zhou, J. (2011). Fault Diagnosis for Smart Grid by a Hybrid Method of Rough Sets and Neural Network. In: Lin, S., Huang, X. (eds) Advances in Computer Science, Environment, Ecoinformatics, and Education. CSEE 2011. Communications in Computer and Information Science, vol 217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23339-5_105
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DOI: https://doi.org/10.1007/978-3-642-23339-5_105
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