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Low voltage shielded cable serves critical roles in controlling and measuring advanced power systems to ensure their reliability and safety. As a consequence, monitoring of the cable is significant, yet conventional monitoring techniques are limited for non-operating power systems for safety causes which eventually affects the reliability of the power system and brings economical loss. In this paper, a contactless signal communication approach via inductive coupler is applied to stepped frequency waveform reflectometry (SFWR) for monitoring operating power systems. Additionally, a long short-term memory (LSTM) based neural network model is adapted for compensating non-linear distortion of the signals yielded by the couplers. Compared to conventional approaches, LSTM has advantages to restore non-linearities of the couplers since it can respond to various non-linear factors such as the dimension of the coupler, the length of the cable, or the position of the signal application. The proposed reflectometry system for monitoring live shielded cable is tested on 75m 2-core shielded cable with two cases of fault emulation, and the efficacy of the proposed technique in real fields is also verified.

High-temperature superconducting (HTS) cable has become one of the most promising technology along with an adaptation of a unique superconducting conductor that allows long-distance transmission of the large volume of electronic power with less power loss. Diagnostic and monitoring technique for the reliable and safe operation of HTS cable system has also become a crucial factor to commercialize the HTS cable system in the regarding field. HTS cable system which needs a cooling process for re-activating superconductors is more affected by the loss of cost and time. This paper is proposing the time-frequency domain reflectometry (TFDR) based diagnostic technique for a live HTS cable system. By adapting inductive couplers, the live HTS cable system and the diagnostic signal would communicate without direct contact. Signal attenuation and distortion during the signal induction are compensated by LSTM-based neural network. The proposed method is applied to an AC 154 kV 600 MVA HTS cable system in and is further verified through Advanced Design System (ADS) software.

To obtain reliable operating of HTS cable system, temperature and pressure are commonly used as condition monitoring parameters. However, the sensors for the parameters are attached only at the terminals or joints of the HTS cable; therefore, the conventional parameters are not suitable to detect the inward change of the HTS cable. In this paper, tangent distance-based template matching (TDTM) coefficient is proposed to monitor the condition of the HTS cable. TDTM technique is applied to time-frequency domain reflectometry (TFDR) which is an advanced fault localization technique, and the TDTM coefficient is calculated during the template matching. A single phase HTS cable is utilized to verify the performance of the TDTM coefficient, and baseline results are measured at 300 K and 77 K in the test bed. It is expected that the proposed TDTM coefficient can be used for stable operation and monitoring of the HTS cable system.

With the advent of commercialization in high temperature superconducting (HTS) cable systems, maintenance techniques for the HTS cable systems are required. In the HTS cable systems, which must maintain the superconducting state during the operation, real-time monitoring is essential. Therefore, in this paper, a monitoring technique for three-phase HTS cables is proposed. The proposed technique, which is based on time-frequency domain reflectometry, has an efficient instrument configuration compared to conventional techniques. Also, by extracting a feature in the time-frequency domain of the monitoring signals, abnormal phase in a three-phase HTS cable can be identified. The proposed technique is demonstrated by experimental data using a real-world three-phase HTS cable. In order to simulate a local quench in the operation of three-phase HTS cable systems, heating elements are utilized in the experimental setup. It is expected that the proposed technique will improve reliability in operating the three-phase HTS cable systems.

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.

In order to improve the performance of the conventional guided wave testing (GWT) with a low sensitivity on damage in pipelines, a new GWT based on time–frequency-domain reflectometry (TFDR) is proposed. TFDR using electromagnetic signals is a diagnostic technique for electrical cables, and the signal is analyzed in both time and frequency domains to enhance the resolution of faults in the electrical cables. Since it is impossible to directly apply the TFDR methodology for electrical cables to the pipelines, this article presents new TFDR methodologies suitable for the ultrasonic characteristics of the pipelines. The proposed technique is demonstrated by experiments using a real-world pipeline. The analysis of the experimental results depending on the changes in the defect size and internal material of the pipeline is provided, and the performance of the proposed technique is confirmed by the experiments.

Monitoring the aging battery cells is important to maintain battery performance. Capacity is a key indicator of battery aging diagnosis, but capacity can be estimated differently depending on the C-rate. Battery impedance monitoring using electrochemical impedance spectroscopy (EIS) is also recognized as a battery diagnostic method. However, EIS is inefficient when diagnosing a large number of individual battery cells due to complex computation and measurement time. In this article, a harmonic analysis method for monitoring aging battery is proposed. The proposed diagnostic technique applies the designed 1-kHz sinusoidal signal, comprising a sinusoidal current component and a direct current component, to the battery. Harmonics are generated when signals passing through nonlinear systems are distorted. A battery is an asymmetric nonlinear system with different bidirectionality. Comparing the simulation and experimental results, we confirmed that the harmonic signatures are the indicators that distinguish between a normal and an aging battery. Moreover, the proposed in situ technique can be applied during charge/discharge cycles, even in the case of an apparent reduction of even harmonics.

In recent years, as reliance on factory automation increases, real-time surveillance techniques for electrical systems have received substantial attention. In particular, the fault diagnosis of shielded cables has become crucial in the industrial sector due to their roles in interconnecting each electrical element. The time–frequency-domain reflectometry (TFDR), which is an advanced cable diagnostic technique, has been used to diagnose various types of shielded cable with high accuracy in fault location. However, in the case of reflected signals with a low signal-to-noise ratio (SNR) caused by any soft faults, the method faces ambiguities in interpreting the presence of failures and locating the faults. Thus, this article proposes an algorithm that simultaneously enhances the fault detection and localization performance of TFDR. In addition, the proposed method provides a statistical model-based threshold for fault detection. The performance of the proposed algorithm is tested via three experiments on actual shielded cables, and the efficacy of the proposed method is verified based on statistical analyses with theoretical discussion.

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 the phasor measurement unit (PMU) based event localization technique. In order to achieve the event localization in real-time manner, the proposed method is based on the measurement of the time required of the event propagation at each PMU. When an event propagates through the transmission network, there exists a time delay to arrive electrical buses in wide-area condition. As an initiated time of the event is unknown information in the practical power systems, time difference of arrival (TDoA) is used to estimate the event location instead of time of arrival. In this regard, the key point of the proposed localization utilizes the comparison of TDoA features that are composed of entire transmission lines to estimate the propagation path. As a result, the directions and time difference values in the shortest path are developed as features under the assumption of event location. Furthermore, a historical event-based network training method is proposed to substitute the system state estimation and calculation of propagation speed. Finally, the comparisons between calculated and measured TDoA features could estimate the event bus, and real-world test shows successful estimation results.

In this paper, new time–frequency-based anomaly detection methodology is proposed for the condition monitoring of high-temperature superconducting (HTS) cable systems. The time–frequency-based anomaly detection methodology includes two indices, which are obtained via cross Wigner–Ville distribution and scattering parameter. For the validation of the proposed methodology, the methodology is applied to an ac 22.9-kV/50-MVA HTS cable system connected to the real power grid network. Furthermore, the changes in dielectric properties of the HTS cable system during the cooling process and the current imbalance failure are monitored. The proposed anomaly detection indices are shown to be able to detect the heat and the current imbalance caused by the malfunction of the joint box. For the application of the two validated indices, new anomaly detection procedure for HTS cable systems is also presented.

When a fault occurs in the high-temperature superconducting (HTS) cable, a fault current consisting of extremely high frequency components is generated and propagated based on the transient response of the HTS cable. Therefore, a simulation model based on high frequency characteristic of the HTS cable is essential to obtain accurate simulation results in the transient fault analysis. In this paper, the result of time domain reflectometry is used to design the model of the HTS cable. To determine model parameters, a general regression neural network based on the kernel density estimation is utilized. After the modeling procedure, the accuracy of the model is evaluated by time-frequency domain reflectometry, whose response depends on the high frequency characteristic of the cable. It is expected that the proposed modeling method can be applied to various application area of HTS cable, such as fault analysis, protection, and diagnostics in the future.

The characteristics of the HTS cable including conductivity, permittivity, and insulation characteristics vary depending on the conditions of temperature and pressure during operations. We propose a diagnostic method that estimates the propagation constant of the HTS cable and detect the location and the degree of the fault using stepped frequency waveform reflectometry. The performance of proposed method is verified by the HTS cable experiment regarding the propagation constant estimation and the fault detection. The results of HTS cable cooled by the liquid nitrogen are also compared with the results at the room temperature to analyze the insulation characteristics change according to the state. The proposed method is expected to be further applied to monitor the condition of HTS cable system regarding temperature and pressure.

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.

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.

High-power industrial rolling mills rely heavily on the sustained operation of cycloconverters, a type of variable-frequency drive. This research proposes a methodology, which observes and diagnoses the operation of cycloconverters as either normal or abnormal by use of time–frequency signature analysis. Various features of the cycloconverter's input current in the time–frequency domain are identified and used to derive parameters that describe these two states in a quantitative manner. A reference model using the parameters is then developed, and comparisons in the time–frequency domain to real data are implemented. Based on these comparisons, a statistical decision boundary is delineated that is used to classify the health status of the cycloconverter.

In this paper, we propose a new time-frequency based analysis method that monitors the state of the high temperature superconducting (HTS) cable system in a real-time manner and detects the current imbalance of HTS cable system. The new time-frequency-based method utilizes the cross Wigner-Ville distribution to analyze the time-frequency localized phase difference of the reflected signal, which varies depending on the insulation characteristics of the HTS cable system. Also, a real-world AC 22.9 kV 50 MVA HTS cable system and a current source are used to validate the performance of the new monitoring method in order to detect current imbalance phenomenon.

International audience; In the era of the Internet of Things (IoT), electrical wiring interconnection systems (EWIS) are massively hosted everywhere in many fields where the transfer of energy and information is a fundamental pillar to guarantee the good performance of a system. In addition, the huge technological and industrial uprising ensured by the emerging fourth Industrial Revolution is forcing electrical machines and instruments toward massive transformations and more complexity than ever before. Accordingly, the health and integrity of any system are essential for the proper operation of any application. Yet, faults and failures are inevitable. They can lead to catastrophic problems on both economical and human levels. Airplane crashes, massive fires, explosions, and so on are examples of such consequences. Accordingly, fault detection, diagnosis, and location have been always substantially important for ensuring safety, security, integrity, and optimal performance for any system. Notably, fault diagnosis of structural cables integrated in dams, bridges, concrete, and so on have also formed a cornerstone in the context of health monitoring of such structures. The same applies for oil and gas pipelines, subsea cables, umbilicals, and so on.

The maintenance of control and instrumentation (C&I) cables is crucial to safety of operating nuclear power plants. Therefore, when an accident occurs, there is a need for an accurate assessment of the impact on the cable's integrity. Unfortunately, most cable diagnostic methods are destructive and real-time assessment of the effect of accidents is not possible. Thus, in this paper, we present an analysis of a specific type of accident, a loss of coolant accident (LOCA), on C&I cables in real-time, based on time–frequency domain reflectometry (TFDR). Because the TFDR is sensitive to the signal-to-noise ratio and distortion of a reflected signal, we apply postprocessing techniques that compensate the dispersion based on the estimated propagation constant and a denoising method using singular value decomposition. The approach is verified by experimentally monitoring condition changes of localized LOCA hot spot in different C&I cables. The results are also validated by comparing with elongation at break test results.

A high-temperature superconducting (HTS) cable system with the 22.9 kV, 50 MVA, and 410 m length is installed and operated at 154 kV Icheon substation of Korea Electric Power Corporation (KEPCO). Unfortunately, it is a difficult task to diagnose and monitor electrical and thermal characteristics of the HTS cable system in a real-time manner. In order to protect operational failures of grid-connected HTS cable systems, this paper proposes time-frequency domain reflectometry (TFDR) and analysis techniques, i.e., time-frequency cross correlation and instantaneous frequency estimation. To verify the performance of the proposed method, the temperature is changed via the cryogenic refrigeration system and the status of the grid-connected HTS cable is monitored via TFDR in a real-time manner.

High temperature superconducting (HTS) cable operates under relatively low temperature and its unique operating condition brings new challenges in the area of maintenance and diagnostics of the cable. As an example, a temperature difference occurs on connection systems between instruments on room temperature and HTS cables which eventually leads to thermal energy loss. The thermal energy loss affects frequency characteristics of the cable which are critical aspects for simulation and maintenance of the HTS cable systems. In this paper, an attempt to reduce thermal loss is introduced with an improved connector applying a Peltier module. The performance of the designed connector is verified with analysis of electromagnetic wave propagation properties of HTS tapes and a cable. The investigation of S -parameters and the proposed connection method is expected to be further applied to various HTS systems in future.

Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

For the electrical insulation of a high-temperature superconducting (HTS) cable, wrapped polypropylene laminated paper (PPLP) tape is typically used. Unfortunately, it is possible that unexpected faults at insulation layers will be present in the cables as a result of either a problematic manufacturing process or an incomplete installation procedure. In order to protect against operational failures of grid-connected HTS cable systems, this paper proposes a nondestructive diagnostic technique, i.e., time-frequency domain reflectometry (TFDR), and focuses on the characteristic of HTS cable that caused the local insulation defects . To verify the performance of the proposed method, detection and localization of local insulation failure via TFDR are compared with traditional time-domain reflectometry. The experiments are conducted at room temperature and under liquid nitrogen in order to check the efficacy of the proposed method in varieties of HTS cable's conditions. In addition, to improve the accuracy of detection and localization, a methodology to analyze incident signals, which are composed of upchirp and downchirp signals, is presented.

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.

A linear chirp reflectometry with chirp stretching processing is used to detect and to locate low-voltage control and instrumentation cable splices and fault. Time delay information in the reflected signal is transformed to the instantaneous beat angular frequency by stretching process and the instantaneous beat angular frequency is estimated by Kalman smoother in order to obtain the high resolution time-frequency spectrum of the nonstationary signal. Based on the estimated instantaneous beat angular frequency, the magnitude and phase difference of the reflection coefficient are estimated by Kalman filtering. To verify the performance of the proposed method, comparative experiments are conducted to detect and to locate the splice under different conditions in comparison with traditional time-domain reflectometry method and the proposed method. In addition, to demonstrate the efficacy of the proposed method, the experiments are carried out for the assessment of state of the shunt and serial faults on cable under test. The location and reflection coefficient of a nominal, water submerged, an opened splice, shunt fault and serial fault ( $10~\Omega , 30 ~\Omega , 50~\Omega , 70 ~\Omega , 90 ~\Omega , 1 ~\text{k}\Omega $ ) are estimated by the proposed method. The proposed method exhibits advantages in that it uses the pulse compression to improve the range resolution and SNR of reflectometer simultaneously, and the proposed technique can accurately assess the state of the fault, which is closed to short fault or open fault.

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.

IEEE Standard 1459 defines the power components based on the fast Fourier series. However, Fourier series assumes the signal is periodic in nature, and provides erroneous assessment of the power components in the presence of transient signals. Therefore, a new method is proposed for the evaluation of the IEEE Standard 1459 power components based on the time-frequency distribution (TFD) and the cross time-frequency distribution (XTFD) of transient signals. The TFD and XTFD preserve simultaneous time and variable frequency information of transient signals, and estimate the instantaneous power components according to IEEE Standard 1459. Results of computer simulated and real-world power quality disturbance case studies justify the effectiveness of the proposed method for the assessment of instantaneous power components under transient conditions.

Based on cross-bispectrum, quadratic-nonlinearity coupling between two vibration signals is proposed and used to assess health conditions of rotating shafts in an AH-64D helicopter tail rotor drive train. Vibration data are gathered from two bearings supporting the shaft in an experimental helicopter drive train simulating different shaft conditions, namely, baseline, misalignment, imbalance, and combination of misalignment and imbalance. The proposed metric shows better capabilities in distinguishing different shaft settings than the conventional linear coupling based on cross-power spectrum.

A new concept of the quadratic-nonlinearity power-index spectrum, QNLPI(f), that can be used in signal detection and classification, is proposed based on the bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the 3-D bicoherence spectrum into 2-D spectrum that quantitatively describes how much of the mean square power at a certain frequency f is generated by nonlinear quadratic interaction between different frequencies. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive gaussian noise, high capability of nonlinear-system identifications, and amplification invariance. Concept of the proposed index and its computational considerations are discussed first using computer-generated data and then applied to real-world vibration data from a helicopter drive train to assess health conditions of different mechanical faults as a part of condition-based maintenance.

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

Time-frequency (t, f) applications are now so widespread that they cannot be comprehensively covered in one volume. For this reason, this chapter aims to further illustrate the (t, f) approach by selecting a few key generic applications of diagnosis and monitoring. The topic is represented by seven sections covering a wide range of diverse applications. One key application is electrical power quality as it is often severely affected by transient disturbances. It is necessary to detect and assess their effect on voltage and current stability. This is achieved by a time-localized frequency analysis where the instantaneous frequency (IF) allows assessing disturbance propagation (Section 15.1). In the automotive industry, the treatment and prevention of knock is a major problem for internal combustion engines as car spark knocks caused by an abnormal combustion may lead to engine damage. The Wigner-Ville distribution is used to optimize the position for placement of knock sensors (Section 15.2). Other applications involve signals that have dispersive spectral delays governed by a power law, such as dispersive propagation of a shock wave in a steel beam and cetacean mammal whistles. A power class of TFDs suitable for such applications is formulated and a methodology is described (Section 15.3). In applications of image processing, image quality may be assessed using a 2D-WVD based measure correlated with subjective human evaluations. It is shown that this SNR measure based on the WVD outperforms conventional SNR measures (Section 15.4). Some general principles of (t, f) diagnosis are then reviewed for medical applications with focus on heart sound abnormality diagnosis (Section 15.5). For machine condition monitoring, a task crucial to the competitiveness of a wide range of industries, the tasks of detecting and diagnosing faults in machines, is made easier using (t, f) approaches such as the WVD, wavelets, and wavelet packets (Section 15.6). The last section presents a specific example of condition monitoring of assets using (t, f) methods that focus on the prevention of steel beam damage (Section 15.7).

In order to properly suppress the harmonic current in a power system, the harmonic similarity metric is developed in this paper and used to establish an efficient strategy for harmonic filter placement. To validate the strategy, an industrial distribution system is analyzed under two harmonic current injection scenarios. It is demonstrated that the proposed strategy has a robust ability to successfully determine the most efficient and effective location for placing a harmonic filter bank based upon the desired objectives. The two harmonic current injection scenarios serve to validate the proposed strategy regardless of the power distribution level at which harmonic current is injected.

A new concept of nonparametric signal detection and classification technique is proposed using mutual information measures in the time-frequency domain. The time-frequency-based self-information and mutual information are defined in terms of the cross time-frequency distribution. Based on time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data obtained from a dedicated condition-based-maintenance experimental test bed. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With imbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information developed and presented herein, machine health classification can be accomplished by use of statistical bounding regions.

Utilities are experiencing premature failures of power cables. In order to prevent electrical outages and to save on repair expenses, a nondestructive and nonintrusive condition assessment technique is highly desirable to evaluate the cable status and to predict the remaining life of a cable. In this paper, the capability of joint time-frequency domain reflectometry (JTFDR) as such a condition assessment technique is studied. The health status of three popular insulations in power system cables - cross-linked polyethylene, ethylene propylene rubber, and silicone rubber - is monitored using the JTFDR in a thermal accelerated aging test. The experimental results show that the JTFDR can successfully monitor the aging process of all three insulations. Then, the results from the JTFDR are compared with the results from the elongation at break (EAB); the results show that the JTFDR technique is comparable with the EAB and has a great potential as a nondestructive and nonintrusive condition assessment technique.

A new methodology for the evaluation of nonfailure operating time (NFOT) in the virtual test bed (VTB) environment is proposed. The proposed method is featured by the application of time-frequency analysis to estimate the parameters of reliability at the earliest stages of design based on the intercoupling of the 1/f noise and the reliability of power system elements. The proposed approach enables one to directly quantify the reliability of the system by the investigation of the physical properties of the nonequilibrium fluctuations.

In this paper, we propose a high-resolution time-frequency-domain reflectometry technique as a methodology of detection and estimation of faults on a wire. This method adopts the time-frequency cross-correlation characteristics of the observed signal in both the time and frequency domains simultaneously. The accuracy of the proposed method is verified with experiments using a radio-guide-type coaxial cable and comparing it with traditional time-domain as well as frequency-domain reflectometry methods. It is clearly shown here that the proposed algorithm produces excellent results compared to the conventional methods for single as well as multiple fault cables.

This paper develops a signal processing, time-frequency analysis-based analytic solution to locate transient capacitor switching disturbances. The flow of transient disturbance energy caused by the capacitor switching is determined by the time and frequency localized phase difference. Cross time-frequency analysis provides time- and frequency-localized phase difference between the transient voltage and current disturbance waveforms which determine the direction of transient disturbance energy flow. The time and frequency localization properties of the proposed scheme allows one to expand the application to complicated power distribution systems without knowledge of system parameters, capacitor size, and configuration. The proposed scheme has been verified by the electromagnetic transients program simulation for all possible spatial locations of the capacitor switching.

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.

In this paper, the effects of visual feedback and aging on postural sway systems and signals are investigated by analyzing the transient phase difference between "input" and "output" which correspond to center of pressure (COP) and center of mass (COM), respectively. In order to analyze the transient phase difference characteristics of COP and COM, a relatively new cross time-frequency analysis technique that provides time- and frequency-localized phase difference information is utilized. The feedback control process in the postural sway is interpreted in terms of a feedback compensator which is characterized in terms of a phase difference. Using the experimental results of the transient phase difference obtained from the cross time-frequency distribution, it is demonstrated that the postural control of young persons are more stable and rely more on visual sensory feedback to stabilize postural control compared to that of the elderly persons.

Per-flow congestion control helps endpoints fairly and efficiently share network resources. Better utilization of network resources can be achieved, however, if congestion management algorithms can determine when two different flows share a congested link. Such knowledge can be used to implement cooperative congestion control or improve the overlay topology of a P2P system. Previous techniques to detect shared congestion either assume a common source or destination node, drop-tail queueing, or a single point of congestion. We propose in this paper a novel technique, applicable to any pair of paths on the Internet, without such limitations. Our technique employs a signal processing method, wavelet denoising, to separate queueing delay caused by network congestion from various other delay variations. Our wavelet-based technique is evaluated through both simulations and Internet experiments. We show that, when detecting shared congestion of paths with a common endpoint, our technique provides faster convergence and higher accuracy while using fewer packets than previous techniques, and that it also accurately determines when there is no shared congestion. Furthermore, we show that our technique is robust and accurate for paths without a common endpoint or synchronized clocks; more specifically, it can tolerate a synchronization offset of up to one second between two packet flows.