• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.2
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• pp.74-80
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• 2014

Generator protection device has to detects an internal fault conditions in generator and abnormal operating conditions must be due to the hazards. Loss of excitation may cause generator itself failure as well as serious operating problem in power system, and then requires an appropriate response of generator protection device. Details modeling of generator control system and analysis of transient states in generator are important for optimal operation in power plants. In addition, the fault simulation data are also used for testing the characteristics of IED. In this paper, the hydro generator control system using PSCAD/EMTDC, visual simulation for power systems, was modeled. The generator control system which is composed of generator, turbine, exciter, governor was implemented. The parameters of generator control system model were obtained from field power plant. Loss of excitation simulations were performed while varying the fixed load. Several signals analysis were also performed so as to analyze transients phenomena.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.320-322
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• 2003

This paper describes a current differential relaying algorithm for a three-phase transformer considering the nonlinear magnetization characteristics of the core. The iron-loss current is obtained from the calculated induced voltage and the core-loss resistance. The magnetizing current is calculated from the estimated core flux and the magnetization curve. The proposed algorithm uses the modified differential current, which is obtained by subtracting the iron-loss current and the magnetizing current from the conventional differential current. The various test results show that the algorithm can discriminate internal fault from magnetic inrush, overexcitation and an external fault.

• Proceedings of the KIEE Conference
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• 2003.07a
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• pp.314-316
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• 2003

To prevent maloperation during magnetic inrush and over-excitation, a current differential relay for power transformers uses harmonic current based restraining or blocking scheme; it also uses dual slope characteristics to prevent maloperation for an external fault with CT saturation. This paper proposes a current differential relaying algorithm for power transformers with an advanced compensation algorithm for the secondary current of CTs. The comparative study was conducted with and without the compensating algorithm. The algorithm can reduce the operating time of the relay in the case of an internal fault and improve security for external faults.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2281-2283
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• 1998

In this paper, we propose a digital differential protection of power transformer using intelligent schemes. Intelligent schemes is based on fuzzy logic and neural networks. To enhance the distinction between fault and inrush of conventional approaches, relaying technique by fuzzy logic and neural networks are used. We used transformer inrush currents, external and internal fault signals, which are obtained from EMTP simulation.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.23-24
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• 2007

Most of modern wind turbines employs a doubly-fed induction generator (DFIG) system because it has many advantages due to variable-speed operation, relatively high efficiency and it small converter size. The DFIG system uses a wound rotor induction machine so that the magnetizing current of the generator can be fed from both the stator and the rotor. This paper presents a protection relaying algorism for DFIG using the DQ equivalent circuits. The induced voltages calculated from the stator and rotor sides are nearly the same in the steady state. They become different in the DQ equivalent circuits during an internal fault. The proposed algorithm compares the inducted voltages estimated from the stator and the rotor circuit converted into the stationary reference frame. If the difference between the induced voltages exceeds the threshold, the proposed algorithm detects an turn-to-turn fault.

• Journal of the Korean Society of Safety
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• v.36 no.3
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• pp.66-73
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• 2021

This report documents the results of an at-power internal events Level 1 Probabilistic Safety Assessment (PSA) for a Korea research reactor (KRR). The aim of the study is to determine the accident sequences, construct an internal level 1 PSA model, and estimate the core damage frequency (CDF). The accident quantification is performed using the AIMS-PSA software version 1.2c along with a fault tree reliability evaluation expert (FTREX) quantification engine. The KRR PSA model is quantified using a cut-off value of 1.0E-15/yr to eliminate the non-effective minimal cut sets (MCSs). The final result indicates a point estimate of 4.55E-06/yr for the overall CDF attributable to internal initiating events in the core damage state for the KRR. Loss of Electric Power (LOEP) is the predominant contributor to the total CDF via a single initiating event (3.68E-6/yr), providing 80.9% of the CDF. The second largest contributor is the beam tube loss of coolant accident (LOCA), which accounts for 9.9% (4.49E-07/yr) of the CDF.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.445-454
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• 2023

Recently, various fault diagnosis studies are being conducted utilizing data from collaborative robots. Existing studies performing fault diagnosis on collaborative robots use static data collected based on the assumed operation of predefined devices. Therefore, the fault diagnosis model has a limitation of increasing dependency on the learned data patterns. Additionally, there is a limitation in that a diagnosis reflecting the characteristics of collaborative robots operating with multiple joints could not be conducted due to experiments using a single motor. This paper proposes an LSTM diagnostic model that can overcome these two limitations. The proposed method selects representative normal patterns using the correlation analysis of vibration and current data in single-axis and multi-axis work environments, and generates residual patterns through differences from the normal representative patterns. An LSTM model that can perform gear wear diagnosis for each axis is created using the generated residual patterns as inputs. This fault diagnosis model can not only reduce the dependence on the model's learning data patterns through representative patterns for each operation, but also diagnose faults occurring during multi-axis operation. Finally, reflecting both internal and external data characteristics, the fault diagnosis performance was improved, showing a high diagnostic performance of 98.57%.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.4
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• pp.292-301
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• 2001

IDDQ Testing is a very effective testing method to detect many kinds of physical defects occurred in CMOS VLSI circuits. In this paper, we consider the most commonly occurring bridging faults in current CMOS technologies and develop pattern generator for IDDQ testing using efficient IDDQ test algorithms. The complete set of bridging faults between every pair of all nodes(internal and external nodes) within circuit under test is assumed as target fault model. The merit of considering the complete bridging fault set is that layout information is not necessary. Implemented test pattern generator uses a new neighbor searching algorithm and fault collapsing schemes to achieve fast run time, high fault coverage, and compact test sets. Experimental results for ISCAS benchmark circuits demonstrate higher efficiency than those of previous methods.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.3
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• pp.87-94
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• 1986

This paper introduce a new test generation method based on built-in data base which is suitable for generating of test set by using PROLOG language. The program presented in this paper deals with all the information required for fault detection from the rules describing output signals and internal signals. Example shows the validity of proposed PROLOG program which results in a effective generation of test set comparable to the conventional D-algorithm.