Waveform characterization of animal contact, tree contact, and lightning induced faults

In this paper signal processing tools are used to uncover common and unique characteristics of faults resulting from animal contacts, tree contacts and lightning. For each fault type a large number of voltage and current waveform data sets measured at monitoring stations on distribution systems are analyzed. The characteristics include but are not limited to the presence of impulse-like oscillations, the number of phases involved, the duration of fault event, the phase angle, the time of day, the spectral content in the time-frequency and time-scale domains, the rate of rise of voltage or current, and the arc voltage. An individual characteristic alone is insufficient to provide an estimate of the fault type. However, by combining common and unique characteristics extracted from a fault event, it may be possible to estimate the fault type accurately.

[1]  Mo-Yuen Chow,et al.  A classification approach for power distribution systems fault cause identification , 2006, IEEE Transactions on Power Systems.

[2]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation , 2000 .

[3]  Qiuye Sun,et al.  Power Distribution Fault Diagnosis Based on Rough-Cloud Sets , 2009, 2009 Asia-Pacific Power and Energy Engineering Conference.

[4]  J. A. Momoh,et al.  Field studies using a neural-net-based approach for fault diagnosis in distribution networks , 1997 .

[5]  A. W. Copley.  High-voltage circuit breakers , 1924, Journal of the A.I.E.E..

[6]  D.D. Sabin,et al.  Using PQ Monitoring and Substation Relays for Fault Location on Distribution Systems , 2007, 2007 IEEE Rural Electric Power Conference.

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  T. Gammon,et al.  Conventional and recommended arc power and energy calculations and arc damage assessment , 2002, IEEE Technical Conference Industrial and Commerical Power Systems.

[9]  B. Koch,et al.  Arc voltage for arcing faults on 25(28)-kV cables and splices , 1993 .

[10]  R. Garzon.  High Voltage Circuit Breakers : Design and Applications , 2002 .

[11]  O. Dag,et al.  Fault classification for power distribution systems via a combined wavelet-neural approach , 2004, 2004 International Conference on Power System Technology, 2004. PowerCon 2004..

[12]  Irene Y. H. Gu,et al.  Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods , 2007, EURASIP J. Adv. Signal Process..

[13]  Vladimir Terzija,et al.  Fault Distance Calculation and Arcing Faults Detection on Overhead Lines Using Single End Data , 2008 .

[14]  R. D. Findlay,et al.  The effect of frequency on the resistance and internal inductance of bare ACSR conductors , 1991 .

[15]  Y. L. Chow,et al.  Grounding resistance of buried electrodes in multi-layer earth predicted by simple voltage measurements along each surface-a theoretical discussion , 1995 .

[16]  A. K. Ghosh,et al.  The classification of power system disturbance waveforms using a neural network approach , 1994 .

[17]  Mo-Yuen Chow,et al.  Analysis and prevention of animal-caused faults in power distribution systems , 1995 .

[18]  Surya Santoso,et al.  Feature analysis and classification methodology for overhead distribution fault events , 2010, IEEE PES General Meeting.