• Structural Engineering and Mechanics
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• v.87 no.5
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• pp.431-450
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• 2023

In this study, it is intended to perform nonlinear time-history analyses of nuclear power plant structures (NPP) under near-fault earthquakes showing directivity pulse and fling-step characteristics. Simulation procedures based on cycloidal pulse and far-fault ground motions are also used to simulate near-fault motions showing forward-directivity and fling-step characteristics and the structural responses are compared with those of the recorded near-fault ground motions. Because it is aimed to determine specifically the pulse type characteristics of near-fault ground motions on NPPs, all the ground motions are normalized to have a PGA of 0.3 g. Depending on the obtained results it can be underlined that although near-fault ground motion has the potential to cause damage mostly on structural systems having larger periods, it may also have noticeable effects on the responses of rigid structures, like NPP containment buildings. On the other hand, simulated near-fault motions can help us to get an insight into the near-fault mechanism as well as an approximate visualization of the structural responses under near-fault earthquakes.

• 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 Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.7
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• pp.9-19
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• 2010

Fuel cell (FC) distributed generation (DG) is gradually becoming more attractive to mainstream electricity users as capacity improves and costs decrease. New technologies including inverters are becoming available to provide a uniform standard interconnection of DGs with an electric power system. Some of the operating conflicts and the effect of DG on power quality are addressed and investigated through simulations on a real distribution network with an FC power plant. The results of these simulations have proved load tracking capability following the real and reactive power change of the load and have shown the flow of overcurrent from an FC power plant during the ground fault of a distribution line.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.579-583
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• 1988

As the power facilities grow up, the optimal operation and the best maintenance of power plant can not be overestimated too much, which can enhance the plant availability and reliability much further. In this respect, fault diagnosis methodologies of dynamic system which is time-varing and strongly nonlinear have been studied. On of them is to use algorithm which is based on time-invariant, linear system, but this is not so nice a method for applying to power Plant. Therefore, the study on other techniques using Artificial Intelligence (AI) is under way. In this paper, the existing ways of fault detection are surveyed and their problems are also discussed.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.13 no.1
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• pp.48-53
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• 2017

We diagnosed the defect using the data obtained from the nuclear power plant simulator. In this paper, we diagnosed faults in the nuclear power plant system for discovery instead of the traditional single-component or device unit. We created the six fault scenarios and used a fault simulator to obtain the fault data. It was extracted pattern from acquired failure data. Neural network model was trained and simple pattern matching algorithm was applied. We presented a simulation result and confirmed that the applied algorithm works correctly.

• Journal of Industrial Technology
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• v.26 no.B
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• pp.145-155
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• 2006

High level of reliability in facility operation is specifically required these days. The goal of this study is to secure a methodology for reliability analysis of hydro-power plant so that an appropriate decision for operation and investment can be made. Fault tree analysis of water supply system within hydro-power plant has been performed in this study. We briefly examined the electric power generation facility and water supply system. We then developed fault tree for the water supply system based on failure modes and effects analysis. We conclude this study and provided future research areas.

• Nuclear Engineering and Technology
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• v.55 no.5
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• pp.1901-1910
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• 2023

The safety-class (1E) digital control system (DCS) of nuclear power plant characterized structural multiple redundancies, therefore, it is important to quantitatively evaluate the reliability of DCS in different degree of backup loss. In this paper, a reliability evaluation model based on T-S fuzzy fault tree (FT) is proposed for 1E DCS of nuclear power plant, in which the connection relationship between components is described by T-S fuzzy gates. Specifically, an output rejection control system is chosen as an example, based on the T-S fuzzy FT model, the key indicators such as probabilistic importance are calculated, and for a further discussion, the T-S fuzzy FT model is transformed into Bayesian Network(BN) equivalently, and the fault diagnosis based on probabilistic analysis is accomplished. Combined with the analysis of actual objects, the effectiveness of proposed method is proved.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.175-176
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• 2015

This paper presents a coordinated control scheme of a doubly-fed induction generator (DFIG)-based wind power plant (WPP) to suppress the overvoltage after a fault clearance. To achieve this, the variation of the terminal voltage at a fault clearance is captured and used to DFIG and WPP controllers. As a result, DFIGs within a WPP suppress the overvoltage rapidly by reducing the reactive power injection. The performance of the proposed scheme was investigated for a 100 MW WPP consisting of 20 units of 5 MW DFIGs for a grid fault. The results show the proposed scheme successfully suppresses the overvoltage at the point of interconnection.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.321-328
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• 1998

A controller and sensor fault tolerant system jot a steam generator is designed with fuzzy logic. A structure of the : proposed fault tolerant redundant system is composed of a supervisor and two fuzzy weighting modulators. A supervisor alternatively checks a controlled and a sensor induced performances to identify Which Part, a controller or a sensor, is faulty. In order to analyze controller induced performance both an error and a charge in error of the system output an chosen as fuzzy variables. The fuzzy logic jot a sensor induced performance uses two variables : a deviation between two sensor outputs and its frequency, Fuzzy weighting modulator generates an output signal compensated for faulty input signal. Simulations show that the : proposed fault tolerant control scheme jot a steam generator regulates welt water level by suppressing fault effect of either controllers or sensors. Therefore through duplicating sensors and controllers with the proposed fault tolerant scheme, both a reliability of a steam generator control and sensor system and that of a power plant increase even mote.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.450-453
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• 1996

This research presents a new method of detecting and diagnosing faults of a power plant. Detection of characteristic wave patterns from multichannel instrumentation signals forms the basis of the proposed approach. The dynamics of 500MW drum-type boiler (Boryung coal-fired plant unit #1 and #2) and its control systems are modeled and simulated to generate diverse operation patterns and fault situations and to utilize them for the development of the fault detection algorithms. The results of the boiler system modeling and simulations show a fairly high agreement when compared with some of the actual plant performance test data.