• KIEE International Transactions on Power Engineering
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• 제5A권1호
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• pp.31-39
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• 2005

This paper illustrates a tie line constrained equivalent assisting generator (TEAG) model considering forced outage rates of transmission systems for reliability evaluation of interconnected power systems. Interconnections between power systems can provide improved levels of reliability. It is expected that the TEAG model developed in this paper will prove useful in the solution to problems related to the effect of transmission system uncertainties in the reliability evaluation of interconnected power systems. It is important that interconnection between power systems can provide the improved levels of reliability. Therefore, It is expected that the TEAG model developed in this study will provide some solution among many problems for interconnected power systems as an optimal tie line capacity and a connected point between assisting systems and assisted system. The characteristics and validity of this developed TEAG considering transmission systems are introduced by case study of three IEEE MRTS interconnected.

• KEPCO Journal on Electric Power and Energy
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• 제7권1호
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2000년도 춘계학술대회논문집
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• pp.52-55
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• 2000

Investigated via a series of finite element process simulation is the effect of diverse process variables on some selected non-dimensional parameters characterizing the strip in hot strip rolling. Then on the basis of these parameters an on-line model is derived for the precise prediction of roll and roll power. The prediction accuracy of the proposed model is examined through comparison with predictions from a finite element process model.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권6호
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• pp.279-285
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• 2006

Power line communication channels are very complicated and models for communication channels vary with the types of electrical equipment, devices and load fluctuations. So, modeling and analysis of power line channels for implementation of power line communication systems is a very important process. Power line channel modeling and simulation are performed based on power system transient simulation models and power system CAD tools to create precise and accurate models. In this paper, a channel modeling and simulation method is proposed for power line communication systems using PSCAD/EMTDC, in which a PN 63 sequence code generator is applied for impulse response of the power line channel in the simulation model.

• 한국멀티미디어학회논문지
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• 제23권3호
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• pp.402-411
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• 2020

Power line noise in electrocardiogram signals makes it difficult to diagnose cardiovascular disease. ECG signals without power line noise are needed to increase the accuracy of diagnosis. In this paper, it is proposed DNN(Deep Neural Network) model to remove the power line noise in ECG. The proposed model is learned with noisy ECG, and clean ECG. Performance of the proposed model were performed in various environments(varying amplitude, frequency change, real-time amplitude change). The evaluation used signal-to-noise ratio and root mean square error (RMSE). The difference in evaluation metrics between the noisy ECG signals and the de-noising ECG signals can demonstrate effectiveness as the de-noising model. The proposed DNN model learning result was a decrease in RMSE 0.0224dB and a increase in signal-to-noise ratio 1.048dB. The results performed in various environments showed a decrease in RMSE 1.7672dB and a increase in signal-to-noise ratio 15.1879dB in amplitude changes, a decrease in RMSE 0.0823dB and a increase in signal-to-noise ratio 4.9287dB in frequency changes. Finally, in real-time amplitude changes, RMSE was decreased 0.3886dB and signal-to-noise ratio was increased 11.4536dB. Thus, it was shown that the proposed DNN model can de-noise power line noise in ECG.

• 전기학회논문지
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• 제58권2호
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• pp.248-255
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• 2009

We proposed a power line channel model. We adopted advantages of other power line channel models to calculate channel responses correctly and simply. Infinite geometric series reduced the calculation complexity of the multipath signal propagation. Description Matrices were also adopted to handle the network topology easily. It represents complex power line network precisely and simply. Newly proposed model considered the effect of the adjacent nodes to channel responses, which have been not considered so far. Several simulations were executed to verify the effect of the adjacent nodes. As a result we found out that it affected channel responses but its effect was limited within certain degree.

• 전력전자학회:학술대회논문집
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• 전력전자학회 1996년도 창립기념 전력전자학술발표회 논문집
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• pp.85-88
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• 1996

New power factor correction(PFC) technique based on the averaged model of boost converter is proposed. Without measurement of input current, power factor correction scheme derived from the averaged model is presented. With the measurements of input voltage and output voltage, the control signal is generated to make the shape of the line current same as the input voltage. The characteristics of input line current distortion is analyzed by considering the generation of duty cycle.

• 한국위성정보통신학회논문지
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• 제12권3호
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• pp.74-79
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• 2017

본 논문에서는 머신러닝 알고리즘 중 하나인 다층 퍼셉트론을 기반으로 전력선통신 시스템에서의 수신 신호를 이용하여 송신단에서 전송한 원신호를 예측하는 시스템 모델을 제안한다. 전력망을 활용한 통신 방식을 사용하는 전력선통신 시스템은 일반적인 통신설로를 활용하는 통신 방식에 비해 잡음이 많다. 이 때문에 전력선통신 시스템의 성능이 저하가 되는 문제가 발생한다. 이를 해결하기 위해 본 논문에서 제안하는 통신 시스템 모델을 이용하면 원신호 예측을 통해 잡음의 영향이 최소화되어 전력선통신 시스템의 성능저하를 완화시킨다. 본 논문에서는 제안한 통신 시스템 모델을 백색 잡음 환경에 적용하여 시뮬레이션을 해봄으로써 원신호가 예측 되는지를 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 하계학술대회 논문집 C
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• pp.1167-1169
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• 1999

This paper presents the new unified power flow controller(UPFC) load flow algorithm using UPFC transmission line constant model. The UPFC transmission line constant model represents a function of transmission line constant(G.B) UPFC's effect in power system. It can easily be incorporated in a load flow program. The algorithm is suited for monitoring the power system state as well as determining the magnitude and Phase angle of UPFC serial voltage source.

• Journal of Power Electronics
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• 제7권2호
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• pp.159-173
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• 2007

In this paper, an intelligent sliding-mode position controller (ISMC) for achieving favorable decoupling control and high precision position tracking performance of permanent-magnet synchronous motor (PMSM) servo drives is proposed. The intelligent position controller consists of a sliding-mode position controller (SMC) in the position feed-back loop in addition to an on-line trained fuzzy-neural-network model-following controller (FNNMFC) in the feedforward loop. The intelligent position controller combines the merits of the SMC with robust characteristics and the FNNMFC with on-line learning ability for periodic command tracking of a PMSM servo drive. The theoretical analyses of the sliding-mode position controller are described with a second order switching surface (PID) which is insensitive to parameter uncertainties and external load disturbances. To realize high dynamic performance in disturbance rejection and tracking characteristics, an on-line trained FNNMFC is proposed. The connective weights and membership functions of the FNNMFC are trained on-line according to the model-following error between the outputs of the reference model and the PMSM servo drive system. The FNNMFC generates an adaptive control signal which is added to the SMC output to attain robust model-following characteristics under different operating conditions regardless of parameter uncertainties and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed intelligent sliding mode position controller. The results confirm that the proposed ISMC grants robust performance and precise response to the reference model regardless of load disturbances and PMSM parameter uncertainties.