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.

...Conversely, external events occur mainly due to animal contacts (squirrels, birds, and snakes), tree contacts and vehicle accidents, as well as natural phenomena such as lightning [2]...
...Five of these features have been proposed in [2]...
...identify the causes of power quality disturbances. Five of them have been proposed in [2]...
...An algorithm to compute the arc voltage during the event is developed in [2][5]...
...The arc voltage expression developed in [2][5] is applicable only to single-phase faults...
...Therefore, based on the time proximity of the events, a cross-correlation technique could be used to identify events captured from different PQM [2]...
...The lightning events close to the strike point can be identified from the presence of high frequency oscillations while the lightning-induced events captured far from the strike point can be identified with time cross-correlation [2]...

Keywords (11)

Waveform characterization of animal contact, tree contact, and lightning induced faults,10.1109/PES.2010.5590214,Saurabh Kulkarni,Duehee Lee,Alicia J.

Citation Context (3)

V. Barrera Núñez, et al. Feature analysis and classification methodology for overhead distribut...

Victor A. Barrera Núñez, et al. SVM-based classification methodology for overhead distribution fault e...

Victor Barrera Núñez, et al. Feature characterization of power quality events according to their un...

References (11)

The classification of power system disturbance waveforms using a neural network approach (Citations: 68)

Field studies using a neural-net-based approach for fault diagnosis in distribution networks (Citations: 7)

A classification approach for power distribution systems fault cause identification (Citations: 33)

Fault classification for power distribution systems via a combined wavelet-neural approach (Citations: 8)

Power Distribution Fault Diagnosis Based on Rough-Cloud Sets (Citations: 1)

Citations (3)

Feature analysis and classification methodology for overhead distribution fault events (Citations: 6)

SVM-based classification methodology for overhead distribution fault events (Citations: 2)

Feature characterization of power quality events according to their underlying causes (Citations: 1)


	The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.


	BibTex \| RIS \| RefWorks Download