In this paper, we review the state of the art in the detection, location, and diagnosis of faults in electrical wiring interconnection systems (EWIS) including in the electric power grid and vehicles and machines. Most electrical test methods rely on measurements of either currents and voltages or on high frequency reflections from impedance discontinuities. Of these high frequency test methods, we review phasor, travelling wave and reflectometry methods. The reflectometry methods summarized include time domain reflectometry (TDR), sequence time domain reflectometry (STDR), spread spectrum time domain reflectometry (SSTDR), orthogonal multi-tone reflectometry (OMTDR), noise domain reflectometry (NDR), chaos time domain reflectometry (CTDR), binary time domain reflectometry (BTDR), frequency domain reflectometry (FDR), multicarrier reflectometry (MCR), and time-frequency domain reflectometry (TFDR). All of these reflectometry methods result in complex data sets (reflectometry signatures) that are the result of reflections in the time/frequency/spatial domains. Automated analysis techniques are needed to detect, locate, and diagnose the fault including genetic algorithm (GA), neural networks (NN), particle swarm optimization, teaching–learning-based optimization, backtracking search optimization, inverse scattering, and iterative approaches. We summarize several of these methods including electromagnetic time-reversal (TR) and the matched-pulse (MP) approach. We also discuss the issue of soft faults (small impedance changes) and methods to augment their signatures, and the challenges of branched networks. We also suggest directions for future research and development.

In this paper, we introduce a new high-resolution reflectometry technique that operates simultaneously in both the time and frequency domains. The approach rests upon time-frequency signal analysis and utilizes a chirp signal multiplied by a Gaussian time envelope. The Gaussian envelope provides time localization, while the chirp allows one to excite the system under test with a swept sinewave covering a frequency band of interest. This latter capability is of particular interest when testing communication cables and systems. Sensitivity in detecting the reflected signal is provided by a time-frequency cross-correlation function. The approach is verified by experimentally locating various types of faults, located at various distances, in RG 142 and RG 400 coaxial cables.

A new high-impedance fault (HIF) detection method using time-frequency analysis for feature extraction is proposed. A pattern classifier is trained whose feature set consists of current waveform energy and normalized joint time-frequency moments. The proposed method shows high efficacy in all of the detection criteria defined in this paper. The method is verified using real-world data, acquired from HIF tests on three different materials (concrete, grass, and tree branch) and under two different conditions (wet and dry). Several nonfault events, which often confuse HIF detection systems, were simulated, such as capacitor switching, transformer inrush current, nonlinear loads, and power-electronics sources. A new set of criteria for fault detection is proposed. Using these criteria, the proposed method is evaluated and its performance is compared with the existing methods. These criteria are accuracy, dependability, security, safety, sensibility, cost, objectivity, completeness, and speed. The proposed method is compared with the existing methods, and it is shown to be more reliable and efficient than its existing counterparts. The effect of choice of the pattern classifier on method efficacy is also investigated.

In order to deal with the nonstationary signatures of phasor measurement units (PMU) signals, this paper presents a wavelet-based detection algorithm. Moreover, for an application to PMU for event detection purpose, it is necessary for us to classify detected events into unexpected real power related accidents, such as generator trip or automated control, such as reactive power injection. The proposed normalized wavelet energy function calculates the root mean square (RMS) of detail coefficients from time-synchronized voltage and frequency that reflect nonstationary occurrence of significant changes in signals. For a robust detection, wavelet-based detection parameter is designed with consideration of nonstationary characteristics of events. Also, there are distinct transients in voltage and frequency caused by different event types, and distinct results are key-idea of event classification. Besides the determination of event occurrences, one can obtain the information of event characteristics that include event types and zonal information of event from the proposed method. Moreover, successful results of detection and classification in real-world cases are presented in this paper.

Fault localization is one of the most significant aspects in the maintenance of high-voltage direct current (HVdc) submarine cables that have unconventional installation characteristics, such as long cable lengths and underwater installation locations. In order to protect and diagnose the cable, an improved fault localization technique, that is, time-frequency domain reflectometry (TFDR) and tangent distance pattern recognition are proposed in this paper. The fault location information of the HVdc submarine cables can be obtained from the tangent distance, to support the results of TFDR. To verify the performance of the proposed method, a commercial HVdc submarine cable is used in the experiments. A test bed is constructed for creating a similar environment with that of the submarine cable and filled with sea water. Both low- and high-impedance faults are emulated in this experiment by local insulation faults with iron, sea water, and air. The theoretical concepts and experimental results of the proposed method are presented. It is expected that the proposed method can improve the reliability of real-world HVdc power systems.

For reasonable power quality assessment of transient disturbances in electric power systems, new transient power quality indices are developed based on a signal processing technique, time-frequency analysis. Based on the time-frequency distribution of a transient disturbance, a set of time-frequency based power quality indices are developed. In this paper, the instantaneous disturbance energy ratio, normalized instantaneous disturbance energy ratio, instantaneous frequency, and instantaneous K-factor are suggested for transient power quality assessment. Time-frequency based power quality indices allow one to quantify the effects of transient disturbances with high-resolution and accuracy.