• Journal of Power Electronics
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• 제18권3호
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• pp.746-756
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• 2018

Harmonic resonance exists in grid-connected inverter systems. In order to determine the network components that contribute to harmonic resonance and the composition of the resonant circuit, sensitivity theory is applied to the resonance characteristic analysis. Based on the modal analysis, the theory of sensitivity is applied to derive a formula for determining the sensitivities of each network component parameter under a resonance circumstance that reflects the participation of the network component. The solving formula is derived for both parallel harmonic resonance and series harmonic resonance. This formula is adopted to a 4-node grid-connected test system. The analysis results reveal that for a certain frequency, the participation of parallel resonance and series resonance are not the same. Finally, experimental results demonstrate that the solving formula for sensitivity is feasible for grid-connected systems.

• Journal of information and communication convergence engineering
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• 제19권4호
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• 전기학회논문지
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• 제64권1호
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• pp.23-28
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• 2015

Two old synchronous condensers in Jeju are being replaced by new machines to operate Jeju AC network with Haenam-Jeju HVDC system stably. Before new synchronous condensers operate on site, voltage stability analysis is conducted to verify stable operation of jeju AC network. Through impedance analysis of the synchronous machine, transformer and ac network, the equivalent circuit is constructed and the voltage drop during start-up is calculated. Then, PSS/E fault analysis is performed to acquire short-circuit capacity according to the generator operation scenarios. Voltage variation when starting synchronous condenser is simulated in PSCAD/EMTDC and satisfies the operating condition of jeju AC network and HVDC #1 system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.75-76
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• 2007

Several kinds of High Temperature Superconducting(HTS) power cables have already been developed and evaluated for use in the utility power network. HTS power cable is expected to be used as a very powerful energy delivery system supplying electric power for densely populated cities in the near future, because HTS power cable is capable of the high current density delivery with low AC loss and the size effect comparing with the conventional cable whose capacity is same. Before applying the HTS power cable to real utility network system analysis should be carried out by some simulation tools. Hereby the electrical power system analysis is very important for the practical use of HTS power devices. In this paper, authors propose a real-time simulation method which incorporates a real HTS tapes into the simulated 22.9kV utility power network system using Real Time Digital Simulator(RTDS). For the simulation analysis, a test sample of HTS tapes was actually manufactured, and the transient phenomenon of HTS power cable system was analyzed in the simulated utility power network.

• Journal of Electrical Engineering and Technology
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• 제2권2호
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• pp.157-164
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• 2007

Diagnosis techniques based on the dissolved gas analysis(DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using fuzzy clustering and a radial basis neural network(RBFNN). In the neural network, fuzzy clustering is effective for selecting the efficient training data and reducing learning process time. After fuzzy clustering, the RBF neural network is developed to analyze and diagnose the state of the transformer. The proposed method measures the possibility and degree of aging as well as the faults occurred in the transformer. To demonstrate the validity of the proposed method, various experiments are performed and their results are presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 A
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• pp.171-173
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• 1997

This paper presents analysis of the radial force density in brushless DC motor of which distribution is not uniform in the axial direction. The analysis considering 3D shape of teeth and overhang is not only important but essential to calculate the radial force density that acts on the teeth of stator, because it is frequent source of vibration and changes at the end of teeth. For the analysis, a new 3D equivalent magnetic circuit network method taking into account movement of the rotor without remesh is proposed. The radial force density is calculated by Maxwell stress tensor and analyzed by discrete Fourier transform.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.229-231
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• 2006

NLPCA(Nonlinear Principal Component Analysis) is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA operates by a feedforward neural network called AANN(Auto Associative Neural Network) which performs the identity mapping. In this work, a sensor fault detection system based on NLPCA is presented. To verify its applicability, simulation study on the data supplied from sensor network is executed.

• KIEE International Transactions on Electrophysics and Applications
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• 제3C권4호
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• pp.130-135
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• 2003

This study describes neural network based dissolved gas analysis using composition patterns of gas concentrations for transformer fault diagnosis. DGA samples were gathered from related literatures and classified into six types of faults and then a neural network was trained using the DGA samples. Diagnosis tests were performed by the trained neural network with DGA samples of serviced transformers, fault causes of which were identified by actual inspection. Diagnosis results by the neural network were in good agreement with actual faults.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.417-421
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• 1991

In electromagnetic transient analysis, complex transmission system should be modelled in detail. But in large system, this full representation of power transmission system has a big burden in many sides such as computation time, modelling efforts, etc. It is very required, therefore, in electromagnetic transients studies to represent parts of a complete system in a reduced or an equivalent form. This paper develops the method from which system equivalents may be derived. The suggested method is of an essentially transient form, and allows travelling wave interaction between the equivalent and explicit transmission network to be modelled.

• Journal of Electrical Engineering and Technology
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• 제13권1호
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• pp.345-351
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• 2018

This paper analyzes a resonant inverter to generate plasma. The resonant inverter consists of a full bridge converter, resonant network and reactor to generate a magnetic field for plasma generation. A plasma load has very distinct characteristics compared to conventional loads. The characteristics of plasma load are analyzed through experimental results. This paper presents the study on the resonant network, which was performed in order to determine how to achieve a constant current gain. Another important contribution of this study is the analysis of drop-out phenomenon observed in plasma loads which is responsible for unpredictable shutdown of the plasma generator that requires stable operation. In addition, the design process for the resonant network of a plasma generator is proposed. The validity of this study is verified through simulations and experimental results.