Fault localization in electrical power systems: A pattern recognition approach

Electrical power system is one of the most complex artificial systems in this world, which safe, steady, economical and reliable operation plays a very important part in social economic development, even in social stability. The fault in power system cannot be completely avoided. In this paper, we developed a method to resolve fault localization problems in power system. In our researches, based on real-time measurement of phasor measurement units, we used mainly pattern classification technology and linear discrimination principle of pattern recognition theory to search for laws of electrical quantity marked changes. The simulation results indicate that respectively study on the phase voltage, positive sequence voltage, negative sequence voltage, phase current, positive sequence current, negative sequence current of single-phase grounding faults and the positive sequence voltage, positive sequence current of three-phase short circuit faults, the pattern classification technology and linear discrimination principle are able to quickly and accurately identify the fault components and fault sections, and eventually accomplish fault isolation. In the study of electrical power systems, pattern recognition theory must have a good prospect of application.

[1]  Songjiao Shi,et al.  A novel distributed approach to robust fault detection and identification , 2008 .

[2]  M.M. Eissa,et al.  A Novel Back Up Wide Area Protection Technique for Power Transmission Grids Using Phasor Measurement Unit , 2010, IEEE Transactions on Power Delivery.

[3]  Arun G. Phadke,et al.  Synchronized Phasor Measurements and Their Applications , 2008 .

[4]  Young Park,et al.  Novel technique for fault location estimation on parallel transmission lines using wavelet , 2007 .

[5]  Tianshu Bi,et al.  A novel hybrid state estimator for including synchronized phasor measurements , 2008 .

[6]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[7]  Chen-Fu Chien,et al.  Rough set theory for data mining for fault diagnosis on distribution feeder , 2004 .

[8]  Yilu Liu,et al.  Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers , 2008 .

[9]  Chun-xia Dou,et al.  Delay-independent excitation control for uncertain large power systems using wide-area measurement signals , 2010 .

[10]  Doo-Kwon Baik,et al.  A study for control of client value using cluster analysis , 2006, J. Netw. Comput. Appl..

[11]  Jovitha Jerome,et al.  Pattern recognition of power signal disturbances using S Transform and TT Transform , 2010 .

[12]  Amany El-Zonkoly,et al.  Applying wavelet entropy principle in fault classification , 2008 .

[13]  Chun-xia Dou,et al.  Fault location using synchronized sequence measurements , 2008 .

[14]  Yuanzhan Sun,et al.  Optimal PMU placement for full network observability using Tabu search algorithm , 2006 .

[15]  Xavier Otazu,et al.  Wavelet based approach to cluster analysis. Application on low dimensional data sets , 2006, Pattern Recognit. Lett..

[16]  Chun-xia Dou,et al.  A WAMS/PMU-based fault location technique , 2007 .

[17]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[18]  J.R. McDonald,et al.  Applying multi-agent system technology in practice: automated management and analysis of SCADA and digital fault recorder data , 2006, 2006 IEEE Power Engineering Society General Meeting.

[19]  Pradipta Kishore Dash,et al.  Pattern recognition based digital relaying for advanced series compensated line , 2008 .

[20]  S. Premrudeepreechacharn,et al.  Measurement placement for power system state estimation by decomposition technique , 2004, 2004 11th International Conference on Harmonics and Quality of Power (IEEE Cat. No.04EX951).

[21]  Zengping Wang,et al.  PCA Fault Feature Extraction in Complex Electric Power Systems , 2010 .

[22]  S. S. Venkata,et al.  A fuzzy expert system for the integrated fault diagnosis , 2000 .

[23]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .