• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.215-224
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• 2003

Since there are various networks and heterogeneity of nodes in Internet, the existing load-sharing algorithms are hardly adapted for use in Internet-based clustering systems. Therefore, in Internet-based clustering systems, a load-sharing algorithm must consider various conditions such as heterogeneity of nodes, characteristics of a network and imbalance of load, and so on. This paper has proposed an expanded-WF algorithm which is based on a WF (Weighted Factoring) algorithm for load-sharing in Internet-based clustering systems. The proposed algorithm uses an adaptive granularity strategy for load-sharing and duplicate execution of partial job for fault-tolerance. For the simulation, the to matrix multiplication using PVM is performed on the heterogeneous clustering environment which consists of two different networks. Compared to other algorithms such as Send, GSS and Weighted Factoring, the proposed algorithm results in an improvement of performance by 55%, 63% and 20%, respectively. Also, this paper shows that It can process the fault-tolerance.

• The Transactions of the Korean Institute of Power Electronics
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• v.3 no.4
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• pp.426-436
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• 1998

Identification of fault current during the operation of a power semiconductor switch and activation of suitable remedial actions are important for reliable operation of power converters. A short circuit is a basic and severe fault situation in a circuit structure such as voltage source converters. This paper presents a new active protection circuit for fast and precise clamping and safe shutdown of fault currents of the IGBTs. This circuit allows operation of the IGBTs with a higher on-state gate voltage, which can thereby reduce the conduction loss in the device without compromising the short circuit protection characteristics. The operation of the circuit is studied under various conditions, considering variation of temperature, rising rate of fault current, gate voltage value, and protection circuit parameters. An evaluation of the operation of the circuit is made using IGBTs from different to confirm the effectiveness of the protection circuit.

• Proceedings of the KIEE Conference
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• 2002.07a
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• pp.487-490
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• 2002

Wind farm paralleled with electric power network can supply the power into a power network not only the normal conditions, but also the fault conditions of distribution network. If the fault happened in the power line with wind farm, the fault current level measured in a relaying point might be lower than that of distribution network without wind turbine generator. Consequently, it is difficult to detect the fault happened in the distribution network connected with wind generator. This paper describes the influence of wind turbine generator on the protective relaying system for detecting the fault occurred in a power line network. Simulation results shows that the fault current depends on the fault impedance, location, and the capacity of wind farm and distribution network load.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.460-466
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• 2002

A reliable fault phase selection algorithm plays a very important role in transmission line protection, Particularly in Extra High Voltage (EHV) networks. The conventional fault phase selection algorithm used the phase difference between positive and negative sequence current excluding load current. But, it is difficult to pick out only fault current since we can not know when a fault occurs and select the fault phase in weak-infeed conditions that dominate zero-sequence current in phase current. The proposed algorithm can select the accurately fault phase using the sum of unit vectors which are calculated by positive-sequence voltage and negative-sequence voltage.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.2
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• pp.103-110
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• 2005

Broken rotor bars in induction motors can be detected by monitoring any abnormality of the spectrum amplitudes at certain frequencies in the motor current spectrum. Broken rotor bar fault detection schemes should rely on multiple signatures in order to overcome or reduce the effect of any misinterpretation of the signatures that are obscured by factors such as measurement noises and different load conditions. Multiple Discriminant Analysis (MDA) and Artificial Neural Networks (ANN) provide appropriate environments to develop such fault detection schemes because of their multi-input processing capabilities. This paper describes two fault detection schemes for broken rotor bar fault detection with multiple signature processing, and demonstrates that multiple signature processing is more efficient than single signature processing.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1916-1922
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• 2009

Introducing distributed generators(DGs) to utility distribution system can cause malfunction of relay on the grid when ground faults or severe load unbalances are occurred on the system. Because DGs interconnected to the grid can contribute fault currents and make bidirectional power flows on the system, fault currents from DGs can cause an interference of relay operation. A directional over current relay(DOCR) can determine the direction of power flow whether a fault occurs at the source side or load side through detecting the phases of voltage and current simultaneously. However, it is identified in this paper that the contributed fault current(Ifdg) from the ground source when was occurred to contribute single-line-to-ground(SLG) fault current, has various phases according to the distances from the ground source. It means that the directionality of Ifdg may not be determined by simply detecting the phases of voltage and current in some fault conditions. The magnitude of Ifdg can be estimated approximately as high as 3 times of a phase current and its maximum is up to 2,000 A depending on the capacity of generation facilities. In order to prevent malfunction of relay and damage of DG facilities from the contribution of ground fault currents, Ifdg should be limited within a proper range. Installation of neutral ground reactor (NGR) at a primary neutral of interconnection transformer was suggested in the paper. Capacity of the proposed NGR can be adjusted easily by controlling taps of the NGR. An algorithm for unidirectional relay was also proposed to prevent the malfunction of relay due to the fault current, Ifdg. By the algorithm, it is possible to determine the directionality of fault from measuring only the magnitude of fault current. It also implies that the directionality of fault can be detected by unidirectional relay without replacement of relay with the bidirectional relay.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.124-126
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• 2001

A reliable fault type identification (phase selection) plays a very important role in transmission line protection, particularly in Extra High Voltage(EHV) networks. The conventional fault type identification algorithm used the phase difference between positive and negative sequence current excluding load current. But, it is difficult to pick out only fault current since we can not know when a fault occurs and identify the fault type in weak-infeed conditions that dominate zero-sequence current in phase current. The proposed algorithm can identify the accurately fault type using the sum of unit vectors which are calculated by positive-sequence votage and negative-sequence voltage.

• 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 Power Electronics
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• v.18 no.5
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• pp.1608-1617
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• 2018

The rapid and correct isolation of faulty submodules (SMs) is of great importance for improving the reliability of modular multilevel converters (MMCs). Therefore, a fast diagnosis method containing fault detection and fault location determination was presented in this paper. An improved incremental predictive model of arm current was proposed to detect failures, and the multi-step prediction method was used to eliminate the negative impact of disturbances. Moreover, a control method was proposed to strengthen the fault characteristics to rapidly locate faulty arms and faulty SMs by detecting the variation rate of the SM capacitor voltage. The proposed method can rapidly and easily locate faulty SMs under different load conditions without the need for additional sensors. The experimental results have validated the effectiveness of the proposed method by using a single-phase MMC with four SMs per arm.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.513-518
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• 2007

In this paper, we propose a fault diagnosis scheme tor induction motor by adopting a hierarchical classifier consisting of k-Nearest Neighbors(k-NN) and Support Vector Machine(SVM). First, some motor conditions are classified by a simple k-NN classifier in advance. And then, more complicated classes are distinguished by SVM. To obtain the normal and fault data, we established an experimental unit with induction motor system and data acquisition module. Feature extraction is performed by Principal Component Analysis(PCA). To show its effectiveness, the proposed fault diagnostic system has been intensively tested with various data acquired under the different electrical and mechanical faults with varying load.