• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.976-983
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• 2006

Multi-resolution Signal Decomposition (MSD) Technique of Wavelet Transform has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge sources present in multi-source PD pattern, usually encountered during practical PD measurement. Employing the Daubechies wavelet, feature were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources and multi-PD soruces.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.65-73
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• 2007

Pattern classification of single and multiple discharge sources was applied using a wavelet image signal method in which a feature extraction was applied using a hidden sub-image. A feature extracting method that used vertical and horizontal images using an MSD method was applied to an averaging process for the scale of pulses for the phase. A feature extracting process for the preprocessing of the input of a neural network was performed using an inverse transformation of the horizontal, vertical, and diagonal sub-images. A back propagation algorithm in a neural network was used to classify defective signals. An algorithm for wavelet image processing was developed. In addition, the defective signal was classified using the extracted value that was quantified for the input of a neural network.

• Journal of international Conference on Electrical Machines and Systems
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• v.2 no.1
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• pp.23-30
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• 2013

This paper proposes a parametric model for inter-turn fault detection in a fault-tolerant permanent-magnet (FTPM) machine, which can predict the effect of the short-circuit fault to various physical quantity of the machine. For different faulty operations, a new effective stator inter-turn fault detection method is proposed. Finally, simulations of vector-controlled FTPM machine drives are given to verify the feasibility of the proposed method, showing that even single-coil short-circuit fault could be exactly detected.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.52-60
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• 2018

The winding of the motor stator coil is broken due to external stress and various factors. If the proper current is not injected when interturn fault(ITF) occurs, the fault can easily be expanded and the motor can be finally destroyed, resulting in many problems with time costs and safety. In this paper, the power loss limit concept, which is the inherent durability of each motor, is applied to secure safety by controlling the total power loss of the motor within the limits. So, we propose an algorithm that can control maximum torque per minimum power loss based on constant torque curve and power loss limit. To verify the proposed method, the simulation and experimental results with an Interior permanent magnet synchronous motor(IPMSM) having an ITF are shown.

• Journal of Power Electronics
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• v.8 no.2
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• pp.181-191
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• 2008

The task performed by induction motors grows increasingly complex in modern industry and hence improvements are sought in the field of fault diagnosis. It is essential to diagnose faults at their very inception, as unscheduled machine down time can upset critical dead lines and cause heavy financial losses. Artificial intelligence (AI) techniques have proved their ability in detection of incipient faults in electrical machines. This paper presents an application of AI techniques for the detection of inter-turn insulation and bearing wear faults in single-phase induction motors. The single-phase induction motor is considered a proto type model to create inter-turn insulation and bearing wear faults. The experimental data for motor intake current, rotor speed, stator winding temperature, bearing temperature and noise of the motor under running condition was generated in the laboratory. The different types of fault detectors were developed based upon three different AI techniques. The input parameters for these detectors were varied from two to five sequentially. The comparisons were made and the best fault detector was determined.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2069-2070
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• 2011

In this paper, the fault diagnosis for a gearbox of BLDC motor. The stator of BLDC motor consists of coil winding so it is easy to cool down and it also has a high reliability. In addition, it doesn't have a brush so it is less trouble and good in maintenance. Coupling with the motor which is the power sources, the gear has a high power transfer efficiency and various rotation speed. The gear gets a high driving force through deceleration. Thus it has been widely used. The gearbox fault detection area has not attracted much attention from electrical engineering community. A few papers describe gearbox fault based on vibration. Gearbox fault is diagnosed through FFT analysis of current and voltage. Fault characteristic frequency side band detected by calculating fault frequency. A threshold value is suggested by comparing normal peak value with fault peak value using detected fault characteristic frequency side band. Experimental results demonstrate that motor current and voltage signal analysis are viable tools in detecting these gear faults. Lower side band(LSB) is bigger than upper side band(USB) in current FFT. LSB and USB are similar in voltage FFT. Finally, fault diagnosis system that can easily detect flaws is developted for gearbox of BLDC motor.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.55-58
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• 2007

본 논문은 교류발전기의 보호방식에 대하여 검토하였고 종래의 교류발전기의 주보호방식인, 비율차동계전방식에 대하여 서술하였다. 87G에서의 고장전후의 전류를 수집하기 위해서 ATP를 이용하여 간소한 발전기 사고 시뮬레이션 방식을 제안하였다. 또 그 사고모의 데이터를 이용하여 고정자권선 보호를 위한 DFT 기반 디지털 비율차동계전기법에 대하여 타당성을 검증하였다.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1612-1614
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• 2003

80pF capacitive couplers were connected to six 6.6kV motor model coil terminals. The voltage applied to the coils were 3.81kv, 4.76 kV and 6.6kV, respectively. These stator coils have various types of artificial insulation defects such as large voids, semi-conductive coating damage and strand insulation fault. Digital PD detector(PDD) and turbine generator analyzer(TGA) were used to measure PD activity. TGA summarizes each plot with two quantities such as the normalized quantity number(NQN) and the peak PD magnitude(Qm). The PD levels in PD were measured with a conventional digital PD detector. Most of the defect mechanism of large motor stator winding can be associated with PD patterns such as internal and slot discharges. PD patterns coincide with PDD and TGA. These instruments have an input bandwidth of 40-400kHz and 0.1-350MHz. Surge testing detects faults in inter-turn winding of high voltage motor model coils.

• Journal of Power Electronics
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• v.6 no.3
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• pp.245-252
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• 2006

This paper presents a method based on neural networks to detect the broken rotor bars and end rings of squirrel cage induction motors. At first, detection methods are studied, and then traditional methods of fault detection and dynamic models of induction motors by using winding function model are introduced. In this method, all of the stator slots and rotor bars are considered, thus the performance of the motor in healthy situations or breakage in each part can be checked. The frequency spectrum of current signals is derived by using Fourier transformation and is analyzed in different conditions. In continuation, an analytical discussion and a simple algorithm are presented to detect the fault. This algorithm is based on neural networks. The neural network has been trained by using information of a 1.1 KW induction motor. This system has been tested with a different amount of load torque, and it is capable of working on-line and of recognizing all normal and ill conditions.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.15 no.1
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• pp.93-101
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• 2002

In this study, the stator form-winding sample coils based on silicone resin and polyimide were made for fault prediction and reliability estimation on the C-Class(200$\^{C}$ ) insulation system of traction motors. The complex accelerative degradation was periodically performed during 10 cycles, which was composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, the condition diagnosis test such as insulation resistance '||'&'||' polarization index, capacitance '||'&'||' dielectric loss and partial discharge properties were investigated in the temperature range of 20 ∼ 160$\^{C}$. Relationship among condition diagnosis tests was analyzed to find a dominative degradation factor and an insulation state at end-life point.