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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.

The demand for cable diagnostics and monitoring techniques has increased significantly in recent decades. Various diagnostic tests such as partial discharge, dielectric loss, and elongation-at-break tests are available in real-world applications. Among the cable diagnostic methods, reflectometry can be used to detect the location of a fault according to the reflected signal at the impedance-discontinuity point. Because it is a nondestructive method and can be applied regardless of the cable type, reflectometry is commonly used for monitoring real-world applications. Time–frequency domain reflectometry, an improved type of reflectometry, is more accurate than conventional reflectometry and employs time–frequency localized signals that are robust to noise. To compensate for the errors occurring in the time–frequency domain reflectometry, several algorithms have been reviewed. In the past, because the signal of time-frequency domain reflectometry contains both time and frequency information, a high-specification signal generator and measurement sensors were required, which made practical application of the method difficult. However, with recent developments in instrumentation and sensing technology, reduction in the instrumentation size and cost facilitates the use of time–frequency domain reflectometry in various industrial applications, e.g., control and instrumentation cables in nuclear power plants, superconducting cables, and submarine cables. This work reviews the use of time-frequency domain reflectometry for cable diagnostics and monitoring in real-world applications. The purpose and results of the experiments on industrial applications are introduced and analyzed. This study also suggests directions for further research to make time–frequency domain reflectometry a diagnostic standard.

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.

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.