• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.2
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• pp.361-370
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• 2024

In this paper, a method was studied for applying a series resonant type fault current limiter that can be manufactured at low cost to the smart grid distribution system. First, the impact of the harmonic components of the short-circuit fault current injected into the series resonance circuit of the fault current limiter on the peak value of the transient response was analyzed, and a methodology for determining the steady-state response was studied using percent impedance-based fault current computation method. Next, the effectiveness of the method was verified by applying it to a test distribution line. The test distribution system using the designed current limiter was modeled using EMTP_RV, and a three-phase short-circuit fault was simulated. In the fault simulation results, it was confirmed that the steady-state response of the fault current accurately followed the design target value after applying the fault current limiter. In addition, by comparing the fault current waveform before and after applying the fault current limiter, it was confirmed that the fault current was greatly suppressed, confirming the effect of applying the series resonance type current limiter to the distribution system.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.12
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• pp.643-650
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• 2000

Using neural network approach, the diagnosis of faults in industrial process that requires observing multiple data simultaneously are studied. Two-stage diagnosis is proposed to analyze system faults. By using neural network, the first stage detects the dynamic trend of each normalized date patterns by comparing a proposed pattern. Instead of using neural network, the difference between stored fault pattern and real time data is used for fault diagnosis in the second stage. This method reduces the amount of calculation and saves storing space. Also, we dealt with unknown faults by normalizing the data and calculating the difference between the value of steady state and the data in case of fault. A model of tank reactor is given to verify that the proposed method is useful and effective to noise.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.449-451
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• 2000

Arcing fault on overhead lines can be detected by amplitude of the arc voltage using numerical algorithm. In the case of transient fault, the arc voltage has any high value. In the case of permanent fault, the arc voltage is near zero. Thus, fault distance estimation should be performed by digital distance relay algorithm[3]. The purpose of this study is to build a structure for modeling of arcing fault detection and fault distance estimation algorithm using Matlab programming. Additionally, this algorithm has been designed in Graphical User Interface(GHI). So, this method using GUI interface of Matlab can reduce the number of simulation steps in modeling the distance relay.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.714-717
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• 1996

This paper presents a fault detection and diagnosis methodologies based on weighted symptom model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom model(WSM) is used to generate those candidates. The weight is determined from dynamic simulation. Using WSMs, the methodology can generate the cause candidates and rank them according to the probability. Secondly, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The standard trends have been generated using dynamic simulation and stored a priori. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.10-11
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• 2006

The fault location is obtained from the distance relay that detects the fault of the transmission line. In this time, transmission line crews track down the fault location and the reasons. However, because of having error at the fault location of the distance relay, there is a discordance between real and obtained fault location. As this reason, the inspection time for finding fault location can be longer. In this paper, we proposed the statistical (regression) analysis method based on each type of relay's the historical fault location data and the real fault distance data to improve the problems. With finding the regression equation based on the regression analysis, and putting the relay fault location into that equation, the real fault distance is calculated. As a result of the Prediction fault location, the inspection time of transmission line can be reduced.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.503-510
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• 2002

This paper presents a fault diagnosis method using neural network-based multi-fault models and statistical method to detect and isolate faults in nonlinear systems. In the proposed method, faults are detected when the errors between the system output and the neural network nominal system output cross a predetermined threshold. Once a fault in the system is detected, the fault classifier statistically isolates the fault by using the error between each neural network-based fault model output and the system output. From the computer simulation results, it is verified that the proposed fault diagonal method can be performed successfully to detect and isolate faults in a nonlinear system.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2106-2117
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• 2017

An effective statistical feature extraction approach of data sampling of fault in the combined transmission system is presented in this paper. The proposed algorithm leads to high accuracy at minimum cost to predict fault location and fault type classification. This algorithm requires impedance measurement data from one end of the transmission line. Modal decomposition is used to extract positive sequence impedance. Then, the fault signal is decomposed by using discrete wavelet transform. Statistical sampling is used to extract appropriate fault features as benchmark of decomposed signal to train classifier. Support Vector Machine (SVM) is used to illustrate the performance of statistical sampling performance. The overall time of sampling is not exceeding 1 1/4 cycles, taking into account the interval time. The proposed method takes two steps of sampling. The first step takes 3/4 cycle of during-fault and the second step takes 1/4 cycle of post fault impedance. The interval time between the two steps is assumed to be 1/4 cycle. Extensive studies using MATLAB software show accurate fault location estimation and fault type classification of the proposed method. The classifier result is presented and compared with well-established travelling wave methods and the performance of the algorithms are analyzed and discussed.

• Nuclear Engineering and Technology
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• v.47 no.2
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• pp.176-186
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• 2015

Pattern classifications have become important tools for fault diagnosis in nuclear power plants (NPP). However, it is often difficult to obtain training data under fault conditions to train a supervised classification model. By contrast, normal plant operating data can be easily made available through increased deployment of supervisory, control, and data acquisition systems. Such data can also be used to train classification models to improve the performance of fault diagnosis scheme. In this paper, a fault diagnosis scheme based on semisupervised classification (SSC) scheme is developed. In this scheme, new measurements collected from the plant are integrated with data observed under fault conditions to train the SSC models. The trained models are subsequently applied to new measurements for fault diagnosis. In comparison with supervised classifiers, the proposed scheme requires significantly fewer data collected under fault conditions to train the classifier. The developed scheme has been validated using different fault scenarios on a desktop NPP simulator as well as on a physical NPP simulator using a graph-based SSC algorithm. All the considered faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis in NPPs.

• 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.