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In this paper unique characteristics of underground cable faults are extracted from the voltage and current waveforms recorded by power quality monitors. These characteristics are used to classify cable faults on the basis of fault duration, specific cable equipment failure and root-cause behind the fault. They are also used to distinguish underground cable faults from other overhead distribution line faults. Waveform signature analysis, wavelet transforms and arc voltage during the fault event is used for cable fault identification and classification.

This user guide is intended to accompany a software package containing a Matrix Laboratory (MATLAB) script and related functions for processing phasor measurement unit (PMU) data. This package and guide have been developed by the National Renewable Energy Laboratory and the University of Texas at Austin. The objective of this data processing exercise is to discover events in the vast quantities of data collected by PMUs. This document attempts to cover some of the theory behind processing the data to isolate events as well as the functioning of the MATLAB scripts. This user guide is divided into two parts. The first part describes the algorithms and mathematical background that the accompanying MATLAB codes use to detect events in PMU data. The second part describes the inputs required from the user and the outputs generated by the scripts.

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.

A network of multiple phasor measurement units (PMU) was created, set up, and maintained by professors and students from the University of Texas at Austin to obtain actual power system measurements for power system analysis. The network is now located and maintained at Baylor University in Waco, Texas. Power system analysis in this report covers a variety of time ranges, such as short-term analysis for power system disturbances and their effects on power system behavior and long-term power system behavior using modal analysis. Modal analysis is the analysis of power system dynamic behavior under excitation from changes in the power system. The PMU data examined in this report is archived at 30 samples per second and is continuously measuring and recording voltage phasor data. Because of the high volume of PMU data generated by the network, it is difficult to localize and analyze power system abnormal events (large disturbance events) of interest that have been recorded by the network.The first objective of this report is to screen the PMU data for events. An algorithm was created using a variety of methods to detect power system events. The algorithm uses fast Fourier transform-, Yule-Walker-, and matrix-pencil-based methods to find events as well as a simple method to detect large swings in the PMU data.The second objective of the report is to identify and describe common characteristics extracted from power system events as measured by PMUs. The report describes category definitions based on visual analysis and extraction of numerical characteristics for each category. Category definitions based on visual inspection consist of two parts: events detected in voltage phase angle signals and frequency signals. In the voltage phase angle, events belong to one or more of the following proposed categories: impulse, transient, or step change. In the frequency, events belong to one or more of the following proposed categories: impulse, transient, or rise or drop in frequency. Some events belong to one category; some events belong to multiple categories, such as frequency drops caused by a sudden loss of generation that can also contain low-frequency oscillations. This type of event belongs to both the drop-in-frequency and transient categories.The numerical characteristics for each category and how these characteristics are used to create selection rules for the algorithm are also described. Trends in PMU data related to different levels and fluctuations in wind power output are also examined. vi This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.