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

Abstract

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. Index Terms-Diagnosis (fault), power distribution faults, power quality, power system monitoring, power system lightning effects, vegetation, animals, wavelet transforms Alicia J. Allen received the B.S. and M.S. degrees from The Department of Electrical and Computer Engineering at The University of Texas at Austin, where she is currently pursuing the Ph.D. degree in energy systems. Her research interests include power quality, renewable energy and synchronized phasor measurements. Surya Santoso (M'96-SM'02) received the M.

Feature analysis and automatic classification of short-circuit faults resulting from external causes

“…This paper aims to find unique features to identify external causes in overhead (animal contacts, tree contacts, lightning‐induced) and underground distribution networks (caused by ingress of water and moisture). The paper extends previous results of the authors about the characterization and classification of events caused by external factors in overhead distribution networks by analyzing new features and including underground lines in the study.…”

“…A set of features extracted from voltage and current waveforms of the events are summarized in this subsection. Some of them (fault insertion phase angle ( FIPA ) and maximum arc voltage) were studied and reported in previous papers , whereas the other ones are proposed in this work (maximum change of the voltage/current magnitude and maximum zero sequence voltage/current). All of the features have been proposed after analyzing the physical principles of underlying causes of the events, and they are candidates to discriminate event causes: Maximum Change of the Voltage Magnitude (∆ V and ∆ V n ): This is the maximum change of the voltage magnitude in absolute value during the fault insertion instant (explained in the sixth item of this list).…”

“…For example, it is known that animal and tree branch contacts with overhead lines can have this phenomena associated. The algorithm, to compute the arc voltage (applicable only for single‐phase faults) during the event, proposed in and has been used in this work. This feature ( V arc ) has been considered only for single‐phase events.It can be observed from Figure that most of the animal contact events have arc voltage values greater than 40% of the prefault voltage, whereas most of the lightning‐induced events are below this threshold.…”

“…They are commonly caused by equipment switching, mis‐operations, or failures . Conversely, externally originated fault events are induced by animal contacts, tree contacts, vehicle accidents, or natural phenomena such as lightning but also ambient conditions (humidity, temperature, etc.) typically affecting cables .…”

Analysis of Cable Screen Currents for Diagnostics Purposes

“…It is believed that in the initial period, the proposed diagnostic method will help to increase the competence of workers, and in the next decade, the solution will be used for online control of cable screens of important cable lines [36]. The proposed method could be a part of an expert system, e.g., a fuzzy expert system that offers additional functionalities, e.g., localization of phase to ground faults or identification of failing components [37,38].…”