A Correlation Analysis Model of Fault Location of Distribution System Based on RS-IA Data Mining

Abstract:

Fault location is an effective way to ensure quickly power recovery after a failure in power distribution system. Bad operation environment of FTU, damaged elements or lost information usually cause variations in fault information for correlation analysis in fault location of power distribution system. A data mining (DM) correlation analysis model based on rough sets (RS) theory and immune algorithm (IA) is proposed in this paper. Firstly, using RS theory to extract domain knowledge, a set of the given variant fault pattern is converted into a decision table in RS. Secondly, an attribute reduction of the decision table is made by using the IA theory, and the intrinsic correlation rules between input vector (condition attribute) and output vector (decision attribute) are mined. Then, the data mining method is used to deal with the distortion of FTU real-time input information. According to the current limit information sequence of section switches, line fault states in each section are judged to realize the fault location in power distribution network. Finally, the feasibility and effectiveness of the fault location model of distribution network based on RS-IA data mining model is verified by simulation.

• Periodical: Applied Mechanics and Materials (Volume 863)
• Pages: 345-354
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.863.345
• Online since: February 2017
• Authors: Zhang You Xu, Yi Fang Su, Pan Zhang, Rong Gang Ge, Fu Jian Chi, Cong Liu, Shu Yan Yu, Lei Jiao Ge*
• Keywords: Data Mining, Distribution Network, Fault Location, Immune Algorithm, Rough Sets

References

[1] M. Luo, H. G. Yang, An improved general matrix algorithm for fault locating in distribution system, Power Syst. Protect. Contr. 40(5) (2012) 64-68.

[2] N. Mei, D. Y. Shi, Z. L. Yang, et al. A practica matrix based fault location algorithm for complex distribution network, Autom. Electr. Power Syst. 31(10) (2007) 66-70.

[3] J. Liu, X. Q. Zhang, X. Q. Tong, et al. Fault Location for Distribution Systems with Distributed Generations, Electr. Power Syst. Autom. 37(2) (2013) 36-42, 48.

[4] Z. N. Wei, H. He, Y. P. Zheng, A novel algorithm for fault location in power distribution network, Autom. Electr. Power Syst. 25(14) (2001) 31-33.

[5] N. Wu, Y. J Jiao, Fault location of distribution network based on plant growth simulation algorithm, Power Syst. Protect. Contr. 37(4) (2009) 23-28.

[6] C. W. Li, Z. Y. He, H. P. Zhang, et al. Fault location for radialized distribution networks based on BPSO algorithm, Power Syst. Protect. Contr. 37(7) (2009) 35-39.

[7] Q. Zhou, B. L. Zheng, R. J. Liao, et al. Fault-section location for distribution networks with DG baded on a hybrid algorithm of particle swarm optimization and differential evolution, Power Syst. Protect. Contr. 41(4) (2013) 33-37.

[8] L. T. Weng, K. P. Liu, X. L. Liu, et al. Chain table algorithm for fault location of complicated distribution network, Trans. China Electrotech. Soc. Autom. Electr. Power Syst. 24(5) (2009) 190-196.

[9] Z. Jingning, Z. Ren, Z. Kai, Application of improved ant colony algorithm in fault-section location of complex distribution network, Electric Utility Deregulation and Restructuring and Power Technologies (DRPT), 2011 4th International Conference on. IEEE, 2011, pp.1067-1071.

DOI: 10.1109/drpt.2011.5994053

[10] P. C. Liu, X. L. Li, Fault-section location of distribution network containing distributed generation based on the multiple-population genetic algorithm, Power Syst. Protect. Contr. 44(2) (2016) 36-41.

[11] Z. N. Wei, H. He, Y. P. Zheng, et al. A refined genetic algorithm for the fault sections location, Pro. CSEE, 24(5) (2002) 52-55.

[12] T. Zheng, Y. M. Pan, K. Y. Guo, Fault location of distribution network based on immune algorithm, Power Syst. Protect. Contr. 42(1) (2014) 77-83.

[13] Z. W. Liao, Y. M. Sun, Data mining technology and its application on power system, Autom. Electric Power Syst. 25(11) (2001) 62-66.

[14] Z. W. Liao, Y. M. Sun, A new approach for fault section diagnosis of distribution system based on data mining model, J. Tianjin Univ. 35(3) (2002) 322-326.

[15] J. Refonaa, M. Lakshmi, V. Vivek, Analysis and prediction of natural disaster using spatial data mining technique, Circuit, Power and Computing Technologies (ICCPCT), 2015 International Conference on. IEEE, 2015, pp.1-6.

DOI: 10.1109/iccpct.2015.7159379

[16] H. G. An, J. J. Koh, A study on the selection of bitmap join index using data mining techniques, Strategic Technology (IFOST), 2012 7th International Forum on. IEEE, 2012, pp.1-5.

DOI: 10.1109/ifost.2012.6357667

[17] H. C. Shu, X. F. Sun, D. J. Si, A rough set approach to distribution network fault location based on fault complain call information, Power Syst. Technol. 28(1) (2004) 64-66.

[18] Z. Zhu, Y. Sun, Application of quantum immune algorithm for fault-section estimation, Power Electronics and Intelligent Transportation System (PEITS), 2009 2nd International Conference on. IEEE, 2009, 1, pp.317-320.

DOI: 10.1109/peits.2009.5407006

[19] J. Zhai, B. Wan, S. Zhang, Probabilistic tolerance rough set model, Wavelet Analysis and Pattern Recognition (ICWAPR), 2015 International Conference on. IEEE, 2015, pp.214-219.

DOI: 10.1109/icwapr.2015.7295953

[20] S. Q. Tian, Y. N. Wang, Y. L. Zhang, et al. Fault location of distribution network based on rough set, J. Jiamusi Univ. (Natural Science Edition), 28(3) (2010) 337-339.

[21] A. R. Hedar, M. A. Omar, A. A. Sewisy, Rough sets attribute reduction using an accelerated genetic algorithm, Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference on. IEEE, 2015, pp.1-7.

DOI: 10.1109/snpd.2015.7176207

[22] Z. Y. Zhu,M. G. Lin, C. M. Xu, Attribute reduction approach based on immune algorithm, Comput. Eng. Sci. 34(1) (2012) 174-177.

[23] L. J. Ge, S. X. Wang, M. Zhang, et al. Power usage management & service platform in smart electricity utilization condition, Electric Power Autom. Equip. 35(3) (2015) 152-156.