• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.1
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• pp.47-52
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• 2016

Fault location identification in the transmission line is an essential part of quick service restoration for maintaining a stable in power system. The application of digital schemes to protection IEDs has led to the development of digital fault locators. Normally, the impedance measurement had been used to for the location detection of transmission line faults. It is well known that the most accurate fault location scheme uses two-ended measurements. This paper deals with the complete design of a fault locator using GPS time-synchronized phasor for transmission line fault detection. The fault location algorithm uses the transmitted relaying signals from the two-ended terminal. The fault locator hardware consists of a Main Processor Unit, Analog Digital Processor Unit, Signal Interface Unit, and Power module. In this paper, sample real-time test cases using COMTRADE format of Omicron apparatus are included. We can see that the implemented fault locator identified all the test faults.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.6
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• pp.955-961
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• 2016

Fault location estimation is an important element for rapid recovery of power system when fault occur in transmission line. In order to calculate line impedance, most of fault location algorithm uses by measuring relaying waveform using DFT. So if there is a calculation error due to the influence of phasor by DC offset component, due to large vibration by line impedance computation, abnormal and non-operation of fault locator can be issue. It is very important to implement the robust fault location algorithm that is not affected by DC offset component. This paper describes an enhanced fault location algorithm based on the DC offset elimination filter to minimize the effects of DC offset on a long transmission line. The proposed DC offset elimination filter has not need any erstwhile information. The phase angle delay of the proposed DC offset filter did not occurred and the gain error was not found. The enhanced fault location algorithm uses DFT filter as well as the proposed DC offset filter. The behavior of the proposed fault location algorithm using off-line simulation has been verified by data about several fault conditions generated by the ATP simulation program.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.1-8
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• 2006

The Steer-By-Wire(SBW) system replaces complex mechanical linkages of the current steering system with electric motors, sensors, and electronic control units. However, the SBW system should guarantee its safety and reliability before commercialization, and therefore, a reliable and robust fault-tolerant technology has to be implemented. This paper proposes a fault-tolerant control algorithm for the SBW system. Based on careful analysis on propagation effects of sensor faults, a reliable fault-tolerant control strategy has been developed. The fault-tolerant controller consists of a fault detection part that monitors and detects faults in the steering wheel and road wheel sensors, and a reconfiguration part that switches to normal sensor signal based on fault detection information. It has been demonstrated by simulation that the proposed algorithm detects sensor faults accurately and enables reliable steering control under various dynamic fault situations.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2619-2627
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• 2009

The ground fault is occupying 70% among the total number of faults in ungrounded distribution power system. When the ground fault occurs in ungrounded system, the fault current is so small that it is hard to detect. But fault handling is very important because to continue power supply during fault conditions may cause the fault spreading and the distribution device in trouble. This paper presents the fault line detection method by using GPT signal detecting zero sequence voltage, and the fault section detection method by detecting whether GPT signal is disappeared or not during shifting normally open switch, which is connecting switch between distribution lines with open state in order to restore the outage area under emergency situation, and during isolating each section one by one which belongs to the fault line. This method is efficient because there is no whole power interruption during the fault section detection, and it is possible to perform both the fault section detection and the service restoration for the outage area at the same time, and it can apply to various distribution system configuration. Program for the fault restoration was developed applying proposed method, and it has been validated by applying to the pilot project of distribution automation system in Vietnam which has the ungrounded distribution system.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Smart Structures and Systems
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• v.29 no.6
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• pp.785-790
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• 2022

Condition monitoring of permanent magnet synchronous motors (PMSMs) and detecting faults such as eccentricity and demagnetization are essential for ensuring system reliability. Motor current signal analysis is the most commonly used precursor for detecting faults in the PMSM drive system. However, the current signature responds sensitively to the load and temperature of the motor, thereby making it difficult to monitor faults in real- applications. Therefore, in this study, a condition monitoring methodology that detects motor faults, including their classification with standstill conditions, is proposed. The objective is to detect and classify faults of PMSMs by using programmable inverter without additional sensors and systems for detection. Both DC and AC were applied through the d-axis of a three-phase motor, and the change in incremental inductance was investigated to detect and classify faults. Simulation with finite element analysis and experiments were performed on PMSMs in healthy conditions as well as with eccentricity and demagnetization faults. Based on the results obtained from experiments, the proposed method was confirmed to detect and classify types of faults, including their severity.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.417-419
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• 2014

• The Transactions of the Korea Information Processing Society
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• v.4 no.1
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• pp.311-323
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• 1997

This paper presents a fast fault simulation system for detecting stuck-at faults in mixed-level combinational logic circuits with gale level and switch -level primitives. For a practical fault simulator, the types are not restricted to static switch-level and/or gate-level circuits, but include dynamic switch-level circuits. To efficiently handle the multiple signal contention problems at wired logic elements, we propose a six-valued logic system and its logic calculus which are used together with signal strength information. As a basic algorithm for the fault simulation process, a well -known gate-level parallel pattern single fault propagation(PPSFP) technique is extended to switch-level circuits in order to handle pass-transistor circuits and precharged logic circuits as well as static CMOS circuits. Finally, we demonstrate the efficiency of our system through the experimental results for switch-level ISCAS85 benchmark combinational circuits and various industrial mixed-level circuits.