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The development of industrial technology is based on electronic devices that are sensitive to power quality. Thus, the demand for high-quality and reliable power supplies is increasing. Voltage sag results in severe problems in the manufacturing process of power quality-sensitive industrial loads. When a fault occurs in a multi-ground power distribution system, the magnitudes of the fault current and voltage sag in the faulted and nonfaulted feeders become high. Hence, installing a superconducting fault current limiter (SFCL) is an effective method of compensating for fault current limitation and voltage sag. This study evaluates the effects of improving the magnitude, duration, and frequency of the voltage sag according to the type of SFCL used. First, a fault in the power distribution system is analyzed using PSCAD/EMTDC, a power system simulation software, according to the fault current-limiting element (CLE) and the type of SFCL. Second, the expected voltage sag frequency caused by a feeder fault in the power distribution system is assessed. Finally, the voltage sag improvement effect according to the CLE and the type of SFCL are compared. The trigger-type SFCL with a resistor as a CLE has been evaluated and found to be effective in improving voltage sag.

A new approach to estimate the direction-of-arrival (DOA) in a Time-Modulated Array (TMA) system with a bipolar squared periodic time-modulating sequence is proposed in this letter. Using such a sequence can increase the power of harmonic components of the received signal. Based on this fact, we formulated a new parameter pertaining to the ratio of the upper and lower sideband first harmonic components of the received signal, to improve the accuracy of the DOA estimation in a low signal-to-noise ratio (SNR) environment. A numerical analysis was performed to verify the effectiveness of the proposed DOA estimation method.

In this paper, the amount of SF6 decomposition gases due to the partial discharge (PD) was studied in the SF6 gas-insulated transformer. The long-term PD degradation experiment was performed while controlling the discharge magnitude using the surface discharge, and the gas generation amount was measured by using gas chromatography for SO2F2, SOF2, SO2, CO, and CF4. In addition, to investigate the relationship between the partial discharge energy and the decomposed gas generation amount, partial discharge energy was calculated by a data processing program and converted to the unit of joule per mole. With the finite element method (FEM), the electric field distribution and SF6 gas decomposition mechanism were explained for the partial discharge energy effect on the gas generation. This study helps understand the relationship between the partial discharge energy and the decomposed gas generation ratio with the experimental results and can be used for the diagnosis of PD and maintenance process for the gas-insulated transformers.

In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in threephase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

This study proposes a new approach to estimate the partial discharge (PD) location for power cables. For the PD location estimation, we estimate the time-of-arrival (TOA) interval between the direct and reflected PD pulses using time-domain reflectometry. In the proposed scheme, we introduce a high-resolution technique known as the multiple signal classification method. This method is a powerful tool that extracts the PD pulses in a noisy environment using the orthogonality of the signal vectors to noise eigenvectors. Simulation results of the theoretical and experimental PD signals show that the proposed scheme accurately estimates the TOA interval of the PD pulses in a noisy environment.

This paper proposes a new diagnosis algorithm to detect broken rotor bars (BRBs) faults in induction motors. The proposed algorithm is composed of a frequency signal dimension order (FSDO) estimator and a fault decision module. The FSDO estimator finds a number of fault-related frequencies in the stator current signature. In the fault decision module, the fault diagnostic index from the FSDO estimator is used depending on the load conditions of the induction motors. Experimental results obtained in a 75 kW three-phase squirrel-cage induction motor show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to a zoom multiple signal classification (ZMUSIC) and a zoom estimation of signal parameters via rotational invariance techniques (ZESPRIT).

The classical multiple signal classification (MUSIC) method has been widely used in induction machine fault detection and diagnosis. This method can extract meaningful frequencies but cannot give accurate amplitude information of fault harmonics. In this paper, we propose a new frequency analysis of stator current to estimate fault-sensitive frequencies and their amplitudes for broken rotor bars (BRBs). The proposed method employs a frequency estimator, an amplitude estimator, and a fault decision module. The frequency estimator is implemented by a zoom technique and a high-resolution analysis technique known as the estimation of signal parameters via rotational invariance techniques, which can extract frequencies accurately. For the amplitude estimator, a least squares estimator is derived to obtain amplitudes of fault harmonics, without frequency leakage. In the fault decision module, the fault diagnosis index from the amplitude estimator is used depending on the load conditions of the induction motors. The fault index and corresponding threshold are optimized by using the false alarm and detection probabilities. Experimental results obtained from induction motors show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to the zoom-based MUSIC algorithm.

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.

Internal pressure of vacuum interrupter (VI) is one of the most important parameters in VI operation and may increase due to the outgassing from the materials inside VI or gas permeation through metal flange or ceramic vessel. The increase of the pressure above a certain level leads to the failures of switching or insulation. Therefore, an effective pressure check of VI is essential and an analysis of partial discharge (PD) characteristics is an effective monitoring method to identify the degree of the internal pressure of VI. This paper introduces a research work on monitoring the internal pressure of VI by analyzing PDs which were measured using a capacitive PD coupler. The authors have developed cost effective capacitive coupler based on the ceramic material that has an excellent insulation properties and the main component of the capacitive coupler is made by SrTiO3. Detectable internal pressure range and distinguishability of the internal pressure of VI were investigated. From the PD tests results, the internal pressure range, from 10 -2 torr to 500 torr, can be monitored by PD measurements using the capacitive coupler and PD inception voltage (PDIV) follows the Paschen's law. In addition, rise time of PD pulse at 13.2kV decreases with the increase of the internal pressure of VI.

The present study describes the considerations on the long-term reliability of the on-line partial discharge (PD) ceramic sensor for thermal power generators. Voltage acceleration aging tests were carried out under continuous and impulsive thermal aging at more than 100℃, considering the practical service environment. Experimental results show that the sensors have a life that could last for more than 100 years, excellent dielectric characteristics, and insulation strength. In addition, the ceramic on-line PD sensors were installed in a thermal power generator in Korea for demonstration. The results of the PD calibration and test voltage application prove that the on-line ceramic sensors have satisfactory performances for on-line PD measurement.

In this paper, a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-case induction motor using an air-gap flux variation analysis is proposed to develop a simple and low cost diagnosis technique. To measure the leakage flux in radial direction, a radial flux sensor is designed as a search coil and installed between stator slots. The proposed method is able to identify two kinds of motor faults by calculating load condition of motors and monitoring abnormal signals those are related with motor faults. Experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed to verify the performance of the proposed method. Induction motors are widely used electrical machines, for their simplicity of construction and reliability. However, they are subject to failures those may be due to production processes or operating conditions. These unexpected failures cause severe damages in industrial processes. Motor reliability working group have announced that percentage failure in induction motors is typically: stator related (38%), rotor related (10%), bearing related (40%), and others (12%) (1). Recently, many researchers have studied diagnosis techniques to predict motor failures at their incipient stage and decide proper replacement time of induction motors. Most of them are focused on the failure prediction method using abnormal signals of failure patterns of motors from current and vibration signals (2)- (4). Although vibration and current analysis are the most powerful methods for diagnosing motor faults, their sensors are occasionally difficult to install where the environment of industrial field is in poor condition. In addition to this installation problem, there are many low priced low voltage induction motors in the field. Therefore, the diagnosis technique should be easy to install their sensors and low cost comparing to the price of motors. This paper proposes a method for detecting broken rotor bar and stator winding fault in a low voltage squirrel-cage induction motor using a radial flux sensor to develop a simple and low cost diagnosis technique. The sensor is designed as a search coil and installed between stator slots during motor production to measure the leakage flux in radial direction at the air-gap. The proposed method consists of a calculating load condition, finding abnormal signals and monitoring those signals related with motor faults to indentify two kinds of motor failures. To demonstrate the performance of the method, experimental results obtained on 7.5kW three-phase squirrel-cage induction motors are discussed.