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.
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.
Owing to the increasing complexity of electrical systems, diagnostic techniques of cables used for connecting electrical elements are essential for system maintenance in order to prevent a failure ...that can cause significant impacts on the overall electrical systems. Multicore structures are typically used as control and instrumentation cables in nuclear power plants, and the failure of the control and instrumentation cables can result in a disaster such as a radiation leak. In this paper, a method for the diagnosis of multicore cables is proposed based on the reflectometry. The diagnosis relates to the classification of defective cores in joint, which is one of the weakest parts in cable systems. The reflected signals obtained through reflectometry are converted into images by an advanced image processing algorithm, and the images are classified using artificial neural networks. The proposed method is demonstrated by experimental data using a real-world multicore cable. In the experiment, the faults are emulated similar to real-world defects using a potentiometer. It is expected that the proposed technique will enhance the stability and reliability of multicore cable systems.
This paper presents a technique for the estimation of oscillation parameters via state-space modeling of synchrophasor data. Due to the spectral leakage of synchrophasors estimated by a Fourier ...transform-based algorithm, the oscillation parameters such as magnitude and frequency will be inherently distorted. Therefore, a state-space model of the instantaneous waveform signal under power system oscillation is derived as an exponentially damped sinusoidal (EDS) signal in order to account for the spectral leakage. The oscillation frequency and magnitude are estimated in real-time by using an unscented Kalman filter (UKF) based on the state-space model. The estimation accuracy performance is validated using the simulation data of sub-synchronous oscillation (SSO). In addition, the efficacy of the proposed method's performance is verified by the application of the proposed method to real-world oscillation events in a wind farm and comparison with the time-frequency analysis.
Subsynchronous resonance (SSR) damping in fixed-speed wind turbine generator systems (FSWTGS) by using two series flexible ac transmission system (FACTS) devices, the thyristor-controlled series ...capacitor (TCSC), and gate-controlled series capacitor (GCSC) are studied in this paper. The former is a commercially available series FACTS device, and the latter is the second generation of series FACTS devices using gate turnoff (GTO) or other gate-commuted switches. The GCSC is characterized by a fixed capacitor in parallel with a pair of antiparallel gate-commuted switches enabling rapid control of series impedance of a transmission line. It is shown that the SSR damping with a GCSC is limited to changing the resonance frequency, in comparison with a fixed capacitor, which may not be adequate to damp out the SSR. Therefore, a supplementary SSR damping controller (SSRDC) is designed for the GCSC. Moreover, it is proven that the GCSC equipped with a well-designed SSRDC can effectively damp the SSR in FSWTGS. In order to verify the effectiveness of the GCSC in SSR damping, its performance is compared with the TCSC, which is an existing series FACTS device. In addition, time-frequency analysis (TFA) is employed in order to evaluate and compare the SSR time-varying frequency characteristics of the GCSC and TCSC. The IEEE first benchmark model on SSR is adapted with an integrated FSWTGS to perform studies, and extensive simulations are carried out using PSCAD/EMTDC to validate the result.
Fault diagnosis has been studied actively across the electrical industry to help maintain the stability of electrical equipment. Among this equipment, shielded cables, which are widely used in ...various industrial sectors, require careful and periodic diagnosis, owing to their poor installation environments and potential for creating huge economic losses. Reflectometry is a representative solution to locate the cable faults. However, conventional reflectometry techniques require prior knowledge about the cable under test, such as the reference wave velocity, total length of the cable, etc. Moreover, the degree of failure cannot be determined using conventional methods. In this paper, a novel reflectometry technique is proposed to locate and evaluate the faults in a cable, without requiring any prior knowledge. A general regression neural network based on the kernel density estimation is utilized with special feature extraction procedures. The proposed method is tested in an actual test bed with two types of emulated faults, and is found to estimate both the fault location and reflection coefficient successfully. It is expected that the proposed method can improve the stability of industrial equipment.
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.
In this article, we propose a fault detection and assessment technique for instrumentation and control cables based on time-frequency image classification using the faster region-based convolutional ...neural network (R-CNN). To train the faster R-CNN while compensating for multiple reflections, the reflected signal estimation is utilized, which divides the reflected signal into the signal propagation along the cable and the reflection from the impedance discontinuity point. Experimental results on two fault scenarios under the circumstance of multiple faults detection and branched networks demonstrate the effectiveness of the proposed method.
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.
A nuclear power plant (NPP) depends on instrumentation and control (I&C) systems to ensure its safe and efficient operation. In particular, I&C cables take on the pivotal role of measuring and ...controlling the critical equipment of the NPP. Thus, it is indubitable that the diagnostic technology of I&C cables for detecting faults and accurately assessing their health status is required for ensuring the safety and reliability of the NPP operation. We propose a diagnostic method that combines fault detection and evaluation algorithm for the I&C cables with stepped-frequency waveform reflectometry with signal propagation and reflection modeling. The signal modeling allows the assessment of the fault with an estimated reflection coefficient by separating the propagation and reflection effects of the measured signal. In short, cable faults are differentiated and quantified regardless of distance. The proposed algorithm is verified by characteristic impedance measurement, various fault detection/evaluation experiments, and the fault evaluation of local accelerated thermal aging cable.
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