• KIEE International Transactions on Power Engineering
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• v.5A no.2
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• pp.138-143
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• 2005

During faults, the voltage and current signals available to the relay are affected by the decaying dc component and harmonics. In order to make appropriate and accurate decisions, most of the relaying algorithms require the fundamental frequency phasor information that is immune to decaying dc effect and harmonics. The conventional Fourier ph as or estimation algorithm is affected by the presence of decaying-exponential transients in the fault signal. This paper presents a modified Fourier algorithm, which effectively eliminates the decaying dc component and the harmonics present in the fault signal. The decaying dc parameters are estimated by means of an out-of-band filtering technique. The decaying dc offset and harmonics are removed by means of a simple computational procedure that involves the design of two sets of Orthogonal digital OFT filters tuned at different frequencies and by creating three off-line look-up tables. The technique was tested for different decay rates of the decaying dc component. It was also compared with the conventional mimic plus the full cycle OFT algorithm. The results indicate that the proposed technique has a faster convergence to the desired value compared to the conventional mimic plus OFT algorithms over a wide range of decay rates. In all cases, the convergence to the desired value was achieved within one cycle of the power system frequency.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1998.06a
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• pp.515-520
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• 1998

In this paper, we propose a new fault tolerant architecture for high availability systems, where for module internal operations both processor modules perform the same tasks at the same time independently of each other while for module external operations both processor modules act actively. That is, operations of synchronization between dual processor modules except clock synchronization are requested only when module external operations are executed. The architecture can not only improve system availability by reducing system reintegration time but also reduce performance degradation problem due to frequent synchronization between dual processor modules. The clock unit consists of a clock generator and a clock synchronization circuit. This supplies a stable clock signal under clock unit disorder of any processor module or rapid clock signal variation. And this architecture achieves system availability and data credibility by designing as symmetrical form.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.1-10
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• 2018

Wide-area monitoring of tree-related high impedance fault (THIF) efficiently contributes to increase reliability of large-scaled network, since the failure to early location of them may results in critical lines tripping and consequently large blackouts. In the first place, this wide-area monitoring of THIF requires managing the placement of sensors across large power grid network according to THIF detection objective. For this purpose, current paper presents a framework in which sensors are distributed according to a predetermined risk map. The proposed risk map determines the possibility of THIF occurrence on every branch in a power network, based on electrical conductivity of trees and their positions to power lines which extracted from spectral data. The obtained possibility value can be considered as a weight coefficient assigned to each branch in sensor placement problem. The next step after sensors deployment is to on-line monitor based on moving data window. In this on-line process, the received data window is evaluated for obtaining a correlation between low frequency and high frequency components of signal. If obtained correlation follows a specified pattern, received signal is considered as a THIF. Thereafter, if several faulted section candidates are found by deployed sensors, the most likely location is chosen from the list of candidates based on predetermined THIF risk map.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.23-26
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• 2005

This paper describes the algorithm for deciding the status of the operating machines in the power plants. It is very important to decide whether the status of the operating machines is good or not in the industry to protect the accidents of machines and improve the operation efficiency of the plants. There are two steps to analyze the status of the running machines. First, we extract the features from the input original data. Second, we classify those features into normal/abnormal condition of the machines using the wavelet transform and the input RMS vector through the K-means algorithm. In this paper we developed the algorithm to detect the fault operation using the K-means method from the sound of the operating machines.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.57-62
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• 2019

In this paper, oneM2M based data monitoring and acquisition system is designed and implemented to measure and transmit the voltage, current, temperature, acceleration and vibration of the motor. The proposed system can detect electrical faults (overcurrent, reverse phase, phase loss, ground fault) and mechanical faults (MC counter, motor operation time, bearing and winding temperature, motor speed, insulation resistance). The system consists of sensor data collection, web server, php, database, wired/wireless communication system. The insulation resistance and the motor speed were measured, and the experimental results were similar for both the test resistance value and the reference input value.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.4
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• pp.313-319
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• 2019

Induction motors connected with a three-phase AC system may malfunction due to reverse phase or open phase faults. Conventional overcurrent relays and overheating relays are used to prevent such accidents; however, their drawbacks include a low response speed and false operation. Therefore, in this study, a digital relay for the reverse-open phase was designed and fabricated. This relay can detect the reverse phase and open phase faults and send a trigger signal to the control circuit. The proposed relay was developed based on a microcontroller. The detection times of the reverse phase and open phase were verified as 320ms and 80ms, respectively. Compared with conventional relays that only protect the motor from one type of fault, the proposed relay can detect both, reverse phase and open phase faults. In addition, the fault detection, identification criterion, and trigger signal patterns can be modified by programming according to the requirements of users.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.76-82
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• 2005

Speech signal has various features of speakers. This feature is extracted from speech signal processing. The speaker is identified by the speaker identification system. In this paper, we propose the speaker identification system that uses the incremental learning based on neural network. Recorded speech signal through the microphone is blocked to the frame of 1024 speech samples. Energy is divided speech signal to voiced signal and unvoiced signal. The extracted 12 orders LPC cpestrum coefficients are used with input data for neural network. The speakers are identified with the speaker identification system using the neural network. The neural network has the structure of MLP which consists of 12 input nodes, 8 hidden nodes, and 4 output nodes. The number of output node means the identified speakers. The first output node is excited to the first speaker. Incremental learning begins when the new speaker is identified. Incremental learning is the learning algorithm that already learned weights are remembered and only the new weights that are created as adding new speaker are trained. It is learning algorithm that overcomes the fault of neural network. The neural network repeats the learning when the new speaker is entered to it. The architecture of neural network is extended with the number of speakers. Therefore, this system can learn without the restricted number of speakers.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2300-2307
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• 2008

This study receives the noise transmitted in a constant audio frequency range through a microphone array in which the noise(like grease in a pan) occurs on the power supply line due to the troublesome partial discharge(arc). Then by going through a series of signal processing of removing noise, this study measures the distance and direction up to the noise caused by the troublesome partial discharge(arc) and monitors the result by displaying in the analog and digital method. After these, it determines the state of each size and judges the distance and direction of problematic part. When the signal sound transmitted by the signal source of bad insulator is received on each microphone, the signal comes only in the frequency range of 20 kHz by passing through the circuit of amplification and 6th low pass filter. Then, this signal is entered in a digital value of digital signal processing(TMS320F2812) through the 16-bit A/D conversion. By doing so, the sound distance, direction and coordinate of bad insulator can be detected by realizing the correlation method of detecting the arriving time difference occurring on each microphone and the algorithm of detecting maximum time difference.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.10
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• pp.859-868
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• 2016

This paper proposes a new global positioning system (GPS) receiver algorithm to improve the positioning accuracy of a transporter using fault detection and isolation techniques from satellite signals. To improve the positioning accuracy, several factors including a feasible number of satellite signals, SNR, NAV Measurement Quality Indicator (mesQI), and Doppler, among others, have been utilized in the proposed algorithm. To increase the number of feasible satellite signals, an erroneous satellite signal has been replaced by the previous one. In conventional approaches, received GPS signals are analyzed and directly determined to be contaminated or not. The only clean signals are utilized for identifying the current location. This fault detection and isolation (FDI) feasibility test is popular for commercial GPS receivers. In the urban environment, especially near a building, the feasible number of satellite signals becomes insufficient to position the transporter. To overcome this problem, satellite signals are efficiently selected and recovered. Additionally, using the proposed GPS receiver algorithm, a feasible number of satellite signals can be increased, thereby improving the positional accuracy. Real world experiments using a transporter that carries blocks in a shipyard have demonstrated the superiority of the proposed algorithm compared to conventional approaches.