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Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase-amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.

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 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.

A time-domain transmission measurement technique for extracting the complex propagation constant of a cross-linked polyethylene cable is proposed. For this extraction, a pulse transmitted through the cable is measured in the time domain and analyzed in the frequency domain. The proposed technique does not use an open termination for reflection, and therefore increases the upper frequency of the bandwidth for extraction; by reducing the pulse width, it decreases the measurement error as compared with an existing time-domain reflectometry method.

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.

This paper presents a two-port time-domain reflectometry(TDR) measurement technique for extracting the complex propagation constant of a cross-linked polyethylene(XLPE) cable. For the extraction, a short pulse transmitted through the cable is measured in the time domain and analyzed in the frequency domain. The propagation constant of a 22.9 kV XLPE cable with a conductor area of 325 is extracted up to a frequency of approximately 2.14 GHz. The measured using a network analyzer and the two-port TDR technique are compared for verification. As a result compared with previous TDR method, the upper possible frequency limit for extracting the propagation constant increases and the measurement error decreases.

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.

A non-toxic boron-containing material, carborane (C2B10H12), has been utilized to deposit amorphous hydrogenated carbon/boron (a-C/B:H) thin films in helium DC glow discharges. The helium plasma was produced at a target pressure of ∼5 mTorr in the pulsed mode at a duty cycle of 0.375 (3 s on/5 s off) or 0.23 (3 s on/10 s off) by using a filament discharge system connected with a carborane evaporation source. Deposited a-C/B:H thin films were characterized by using variable angle spectroscopic ellipsometry, scanning electron microscopy, Auger electron spectroscopy, and X-ray photoelectron spectroscopy. The a-C/B:H thin films have soft polymer-like characteristics with optical constants of n = 1.65 and k = 10−3 at 632 nm, and the B/C ratio inside the bulk was ∼2 for an ∼60% boron contents. The evaporation characteristics of carborane powder under vacuum were also investigated.

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.

First boronization in KSTAR is reported. KSTAR boronization system is based on a carborane (C2B10H12) injection system. The design, construction, and test of the system are accomplished and it is tested by using a small vacuum vessel before it is mounted to a KSTAR port. After the boronization in KSTAR, impurity levels are significantly reduced by factor of 3 (oxygen) and by 10 (carbon). Characteristics of a-C/B:H thin films deposited by carborane vapor are investigated. Re-condensation of carborane vapor during the test phase has been reported.

The partial discharge (PD) measurement is a very effective method to assess the winding insulation condition of high-voltage machines, since most of the insulation failure processes are directly or indirectly caused by PD. On-line PD measurements, which can detect insulation defects of winding in the early stages on rotating machines in operation, have been accepted as the most important technique. The epoxy mica capacitive coupler is currently and extensively used for on-line detection of PD pulses of high-voltage rotating machines. To evaluate the feasibility of developing a capacitive coupler that is easier to manufacture at a lower cost compared to epoxy mica couplers, a 100 ㎊ capacitive coupler made of ceramic material is designed, fabricated and tested for on-line PD measurements of 23 ㎸ electrical machines. A series of electrical tests and accelerated aging tests are performed on the ceramic coupler to evaluate the performance requirements, long-term reliability and thermal stability for in field application. The test results show that the newly developed ceramic coupler provides equal and improved performance at a lower cost compared to epoxy mica couplers, and estimated voltage life is anticipated to surpass 100 years.