• Journal of Institute of Control, Robotics and Systems
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• v.4 no.1
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• pp.123-133
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• 1998

In this paper, an algorithm of fault detection and diagnosis during operation of induction motors under the condition of various loads and rates is investigated. For this purpose, the spectrum pattern of input currents is used in monitoring the state of induction motors, and by clustering the spectrum pattern of input currents, the newly occurrence of spectrum patterns caused by faults are detected. For the diagnosis of the fault detected, a fuzzy fault tree is designed, and the fuzzy relation equation representing the relation between an induction motor fault and each fault type, is solved. The solution of the fuzzy relation equation shows the possibility of occurence of each fault. The results obtained are summarized as follows : (1) Using clustering algorithm by unsupervised learning, an on-line fault detection method unaffected by the characteristics of loads and rates is implemented, and the degree of dependency for experts during fault detection is reduced. (2) With the fuzzy fault tree, the fault diagnosis process become systematic and expandable to the whole system, and the diagnosis for sub-systems can be made as an object-oriented module.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.6
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• pp.22-29
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• 2002

The conventional neon transformer systems are very bulky and heavy because it consist of leakage type transformers made of silicon steel plates. In addition, it has problems in noise by a neon transformer and in possibilities of fire and electrical shock when neon tubes are destroyed. A protection circuit is designed for all types of neon transformer loaded with one or more neon tubes. Whenever the neon tube fails to be started up, comes to the life end, encounters faults with open-circuits at the output terminals of the neon transformer, the protection circuit will be initiated to avoid more critical hazards. The input of the transformer is automatically cut off when the abnormal condition occurs, preventing waste of no-load power. To improve such problems, in this paper, a new type of neon power supply systems for neon tube is designed and implemented using inverter type circuits and a newly designed lightweight transformer. In the developed neon transformer system, a 60［Hz］power input is converted to 20［KHz］high frequency using half-wave inverters, thereby the transformer reduces its size by 1/5 in volume and 1/10 in weight.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.457-465
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• 2011

The fault analysis and detecting algorithm for a 3 phase diode rectifier is proposed. The 3 phase dioderectifier is used for the AC power rectifier of the PWM inverter. The input power or diode faults cause theripples of the DC voltage, degradation of the control performance and life shortening of the DC link capacitor.In this paper, the ripple of the DC voltage is mathematically analyzed for the earth fault of input power andopen circuit fault of the diode, respectively. The fault detection and type of fault can be obtained by comparingthe average DC voltage and the instant DC voltage which is sampled with 6 times of grid frequency. Theproposed method can be easily applicable and doesn't require additional circuit. The experimental and simulationresults are presented to verify the validity of the proposed method.

• Journal of Electrical Engineering and Technology
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• v.6 no.2
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• pp.154-160
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• 2011

A new algorithm for estimating the fundamental frequency of power system signals is presented. The proposed algorithm consists of two stages: orthogonal decomposition and a complex Prony analysis. First, the input signal is decomposed into two orthogonal components using cosine and sine filters, and a variable window is adapted to enhance the performance of eliminating harmonics. Then a complex Prony analysis that is proposed in this paper is used to estimate the fundamental frequency by approximating the cosine-filtered and sine-filtered signals simultaneously. To evaluate the performance of the algorithm, amplitude modulation and harmonic tests were performed using simulated test signals. The performance of the algorithm was also assessed for dynamic conditions on a single-machine power system. The Electromagnetic Transients Program was used to generate voltage signals for a load increase and single phase-to-ground faults. The performance evaluation showed that the proposed algorithm accurately estimated the fundamental frequency of power system signals in the presence of amplitude modulation and harmonics.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.10
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• pp.1516-1522
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• 2017

We propose a novel monitoring system for diagnosing crack faults of the wind turbine using image information. The proposed method classifies a normal state and a abnormal state for the blade parts of the wind turbine. Specifically, the images are input to the proposed system in various states of wind turbine rotation. according to the blade condition. Then, the video of rotating blades on the wind turbine is divided into several image frames. Motion vectors are estimated using the previous and current images using the motion estimation, and the change of the motion vectors is analyzed according to the blade state. Finally, we determine the final blade state using the Support Vector Machine (SVM) classifier. In SVM, features are constructed using the area information of the blades and the motion vector values. The experimental results showed that the proposed method had high classification performance and its $F_1$ score was 0.9790.

• Journal of Power Electronics
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• v.8 no.1
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• pp.10-22
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• 2008

The wind-driven doubly fed induction generator (DFIG) is currently under pressure to be more grid-compatible. The main concern is the fault ride-through (FRT) requirement to keep the generator connected to the grid during faults. In response to this, the paper introduces a novel model and new control scheme for the DFIG. The model provides a means of direct stator power control and considers the stator transients. On the basis of the derived model, a robust linear quadratic (LQ) controller is synthesized. The control law has proportional and integral actions and takes account of one sample delay in the input owing to the microprocessor's execution time. Further, the influence of the grid voltage imperfection is mitigated using frequency shaped cost functional method. Compensation of the rotor current pulsations is proposed to improve the FRT capability as well as the generator performance under grid voltage unbalance. As a consequence, the control system can achieve i) fast direct power control without instability risk, ii) alleviation of the problems associated with the DFIG operation under unbalanced grid voltage, and iii) high probability of successful grid FRT. The effectiveness of the proposed solution is confirmed through simulation studies on 2MW DFIG.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1861-1868
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• 2008

In this paper, Passive Telemerty RF Sensor System using Unscented Kalman Filter algorithm(UKF) is proposed. General Passive Telemerty RF Sensor System means that it should be "wireless", "implantable" and "batterless". Conventional Passive Telemerty RF Sensor System adopts Integrated Circuit type, but there are defects like complexity of structure and limit of large power consumption in some cases. In order to overcome these kinds of faults, Passive Telemetry RF Sensor System based on inductive coupling principle is proposed in this paper. Because passive components R, L, C have stray parameters in the range of high frequency such as about 200[KHz] used in this paper, Passive Telemetry RF Sensor System considering stray parameters has to be derived for accurate model identification. Proposed Passive Telemetry RF Sensor System is simple because it consists of R, L and C and measures the change of environment like pressure and humidity in the type of capacitive value. This system adopted UKF algorithm for estimation of this capacitive parameter included in nonlinear system like Passive Telemetry RF Sensor System. For the purpose of obtaining learning data pairs for UKF Algorithm, Phase Difference Detector and Amplitude Detector are proposed respectively which make it possible to get amplitude and phase between input and output voltage. Finally, it is verified that capacitive parameter of proposed Passive Telemetry RF Sensor System using UKF algorithm can be estimated in noisy environment efficiently.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.7
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• pp.878-882
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• 2004

In this paper, we propose a hierarchical hybrid neural network for detecting faults of induction motor. Implementing the classifier based on the input and output data, we apply appropriate transform and classification method at each step. In the proposed method, after obtaining the current of state of motor for each period, we transform it by Principle Component Analysis(PCA) to reduce its dimension. Before the training process, we use the conditional Fuzzy C-means(FCM) for obtaining the initial parameters of neural network for more effective learning procedure. From the various simulations, we find that the proposed method shows better performance to detect and diagnosis of induction motor and compare than other methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.7
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• pp.365-374
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• 2004

The relay measures the frequency of the power system in order to detect faults and separate them from the system. Many estimation algorithms for the relay have been proposed to accurately measure the frequency. This paper proposes a new frequency measurement method using the digital phase locked-loop(DPLL) for the relay of the power system. The proposed method is configured with a DPLL scheme and verified through computer simulations and experimental tests. In order to cope with noises in the power system, filters are included in the input signal processing part and the frequency comparator. MATLAB is used for computer simulations and an experimental setup with a CPU and an FPGA(Field Programmable Gate Array) is constructed. The loop filter of the DPLL is run in the CPU software In adjust parameters and others are in the FPGA. Experimental tests are performed lot a function generator and the power system. Results show that the proposed method is appropriate to the frequency measurement for the relay.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.426-435
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• 1999

A high impedance fault(HIF) is one of the serious problems facing the electric utility industry today. Because of the high impedance of a downed conductor under some conditions these faults are not easily detected by over-current based protection devices and can cause fires and personal hazard. In this paper a new method for detection of HIF which uses adaptive neuro-fuzzy inference system (ANFIS) is proposed. Since arcing fault current shows different changes during high and low voltage portion of conductor voltage waveform we firstly divided one cycle of fault current into equal spanned four data windows according to the mangnitude of conductor voltage. Fast fourier transform(FFT) is applied to each data window and the frequency spectrum of current waveform are chosen asinputs of ANFIS after input selection method is preprocessed. Using staged fault and normal data ANFIS is trained to discriminate between normal and HIF status by hybrid learning algorithm. This algorithm adapted gradient descent and least square method and shows rapid convergence speed and improved convergence error. The proposed method represent good performance when applied to staged fault data and HIFLL(high impedance like load)such as arc-welder.