• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2012년도 춘계학술발표대회 논문집
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• 전기전자학회논문지
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• 제22권3호
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• pp.754-758
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• 2018

전력계통에서 해석 하는 방법종류로 정적해석과 동적해석 그리고 온라인 해석과 오프라인 해석으로 나뉜다. 기존의 계통 해석하는 방법으로는 오프라인 정적해석을 이용하며 PSS/E에서도 정적해석을 주로 사용한다. 하지만 우리가 사용하고 있는 실제 계통에서는 실시간으로 값이 변한다. 정확한 해석을 하기 위해 실시간으로 해석이 가능한 온라인 해석과 동적해석이 요구되며 이에 따라 EMS(Energy Management System)에 대한 중요도가 높아지고 있다. 본 논문에서는 다양한 EMS시스템 중에서도 실시간으로 취득 점으로부터 데이터를 받아와 해석 할 수 있는 정적 온라인 해석인 상태추정을 다룰 것이다. 오늘날 DC부하, DC배전망, 신재생에너지 등 다양한 분야에서 DC계통이 확산되고 있다. 이처럼 DC계통에 많은 관심과 이목이 집중되고 있다. 본 논문에서는 DC계통에 접목시킬 수 있는 상태추정에 대한 해석과 사례연구를 통한 사용가능성에 대해 연구하였다.

• 대한전기학회논문지:전력기술부문A
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• 제52권8호
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• pp.471-480
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• 2003

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. Among the reasons for its complexity are the system size, conflicting requirements of estimator accuracy, reliability in the presence of transducer noise and data communication failures, adaptability to change in the network topology and cost minimization. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs) which is widely used in areas such as: optimization of the objective function, learning of neural networks, tuning of fuzzy membership functions, machine learning, system identification and control. This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 A
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• pp.286-288
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• 2004

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Genetic Algorithms (GAs). This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Genetic Algorithms (GAs) in the Harmonic State Estimation (HSE).

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 A
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• pp.80-84
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• 2000

Network parameters in power systems are indispensable for all of power system engineering studies, including the power flow calculation and the state estimation. The network parameters required for the studios, in general, are estimated by using several estimation techniques, since it Is very difficult to measure. To improve the estimation accuracy of the network parameters, this paper adopt the synchronized phasor measurements which are acquired from the Phasor Measurement Unit with built-in GPS receiver. In this paper, the parameter estimation problem is formulated with over-determined nonlinear measurement equations and solved with Newton-Raphson method and pseudo-inverse. The effectiveness of the proposed parameter estimation with the synchronized phasor measurements is verified through some case studies with IEEE sample system. The results are very promising.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 D
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• pp.1180-1183
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• 1997

This paper addresses the issues of the external system modeling and power system state estimation with the external model. A set of significant measurements is identified in the external system based on their sensitivities on the tie-lines. These measurement data are transferred to internal system and updated in real-time. The state estimator is run for different loading conditions by using the actual values for the internal system and selected significant external system measurements while keeping the rest of the external system measurements at their base case values. Simulation results are presented using the IEEE 118 bus system as an example.

• Journal of Power Electronics
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• 제16권1호
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• pp.238-248
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• 2016

The state-of-charge (SOC) and state-of-health (SOH) estimation of batteries play important roles in managing batteries for automotive applications. However, an accurate state estimation of a battery is difficult to achieve because of certain factors, such as measurement noise, highly nonlinear characteristics, strong hysteresis phenomenon, and diffusion effect of batteries. In certain vehicular applications, such as idle stop-start systems (ISSs), significant errors in SOC/SOH estimation may lead to a failure in restarting a combustion engine after the shut-off period of the engine when the vehicle is at rest, such as at a traffic light. In this paper, a dual extended Kalman filter algorithm with a dynamic equivalent circuit model of a lead-acid battery is proposed to deal with this problem. The proposed algorithm adopts a battery model by taking into account the hysteresis phenomenon, diffusion effect, and parameter variations for accurate state estimations of the battery. The validity of the proposed algorithm is verified through experiments by using an absorbed glass mat valve-regulated lead-acid battery and a battery sensor cable for commercial ISS vehicles.

• 전력전자학회논문지
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• 제13권5호
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• pp.360-368
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• 2008

하이브리드 전기자동차용 리튬이온 배터리 시스템의 모델링 및 모델기반 상태 관측기 설계에 관한 연구를 수행하였다. 배터리의 동적 특성을 모사하기 위한 모델은 전기적 등가회로로 단순화하여 제안하였다. 모델 파라미터는 최소 자승 이론에 의거 다양한 운전조건에 따라 추정하였고, 실험 검증하였다. 검증된 모델을 기반으로 하는 비례-적분(PI) 제어의 상태 관측기를 구성하였고, 제어 이득 선정방법을 제안하였다. 제안된 상태 관측기의 강인성은 시뮬레이션 및 실험에 의거 다양한 운전조건 변동에 따른 민감도를 분석하여 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 A
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• pp.149-150
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• 2006

The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. In particular, the number of harmonic instruments available is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents a new HSE algorithm which is based on an optimal placement of measurement points using Intelligent Algorithms (IAs). This HSE has been applied to the Simulation Test Power System for the validation of the new HSE algorithm. The study results have indicated an economical and effective method for optimal placement of measurement points using Intelligent Algorithms (IAs) in the Harmonic State Estimation(HSE).

• 대한전기학회논문지
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• 제43권6호
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• pp.877-884
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• 1994

In the power system state estimation, the J(x)-index test and normalized residuals ${\gamma}$S1NT have been the presence of bad measurements and identify their location. But, these methods require the complete re-estimation of system states whenever bad data is identified. This paper presents back-propagation neural network medel using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional mehtods and simulation results show the geed performance in the bad data identification based on the neural network under sample power system.