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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.

Due to the efforts of digitization of power grids and integration of online diagnosis systems, today's power transformers have been promoted to deploy seamless communications and information technology, which potentially presents growing threats from cyber-attacks. This paper provides security vulnerabilities and potential threats of power transformers in cyber-physical environments through a security threat modeling methodology. The proposed security threat modeling includes three steps: 1) system identification, 2) threat modeling, 3) and attack modeling. The devised security threat model can identify potential vulnerabilities and threats as well as simulate attack success rate, which will be used to develop device-level defense strategies to protect power transformers from malicious cyber-attacks, ensuring the secure utilization of the cyber-physical power transformers in modern power infrastructures.

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.

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 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.

This paper introduces an evaluation code, which can numerically express the noise possessing degree of signals. By using this code, the best kind and setting of noise suppressing techniques can be chosen automatically. This code is applied to three kinds of specific denoising techniques; those are simple noise removing method in the count versus phase distribution, fuzzy logic method based on noise type in magnitude versus phase plot, and lastly, the technique using grouping characteristics of PD pulses in 3D plot of magnitude versus phase versus cycle. The algorithm shows good performance in the various real PD signals measured from various high voltage apparatuses in Korea.

This paper introduces an evaluation code, which can numerically express the noise possessing degree of signals. By using this code, the best kind and setting of noise suppressing techniques can be chosen automatically. This code is applied to three kinds of specific denoising techniques; those are simple noise removing method in the count versus phase distribution, fuzzy logic method based on noise type in magnitude versus phase plot, and lastly, the technique using grouping characteristics of PD pulses in 3D plot of magnitude versus phase versus cycle. The algorithm shows good performance in the various real PD signals measured from various high voltage apparatuses in Korea.