• 한국산학기술학회논문지
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• 제8권5호
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• pp.1020-1025
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• 2007

전력시스템을 안정적이고 경제적으로 운영하기 위하여 EMS(energy management system)와 SCADA(supervisory control and data acquisition) 시스템이 사용되고 있다. EMS 내의 조류계산, 상정고장해석, 안전도해석과 같은 다양한 기능들의 신뢰성을 높이기 위해서는 정확한 데이터의 확보가 필수적이다. EMS 내에서 상태추정이 이와 같은 역할을 수행할 수 있으며, 본 논문에서는 정확한 상태추정을 위한 가관측성 해석 및 불량데이터 처리 프로그램을 개발하였다. 기본적인 알고리즘을 설명하고 사례연구를 통해 제안된 기법의 타당성을 검증하였다.

• 전력전자학회논문지
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• 제20권4호
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• pp.344-350
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• 2015

A battery management system requires accurate information on the battery state of charge (SOC) to achieve efficient energy management of electric vehicle and renewable energy systems. Although correct SOC estimation is difficult because of the changes in the electrical characteristics of the battery attributed to ambient temperature, service life, and operating point, various methods for accurate SOC estimation have been reported. On the basis of piecewise linear (PWL) modeling technique, this paper proposes a simple SOC observer for lithium-polymer batteries. For performance evaluation, the SOC estimated by the PWL SOC observer, the SOC measured by the battery-discharging experiment and the SOC estimated by the extended Kalman filter (EKF) estimator were compared through a PSIM simulation study.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2014년도 추계학술대회 논문집
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• pp.60-61
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• 2014

Lithium-Ion batteries have become the best tradeoff between energy, power density and cost of the energy storage system in many portable high electric power applications. In order to manage the battery efficiently State of Charge (SOC) of the battery needs to be estimated accurately. In this paper a model-based approach to estimate the SOC of the Lithium-Ion battery based on the estimation of the battery impedance is proposed. The validity and feasibility of the proposed algorithm is verified by the experimental results.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.131-133
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• 1993

In the power system state estimation, the J(x)-index test and normalized residuals $r_N$ have been used to detect 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 model using autoregressive filter for identification of bad measurements. The performances of neural network method are compared with those of conventional methods and simulation results show the good performance in the bad data identification based on the neural network under sample power system.

• 전기학회논문지
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• 제64권11호
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• pp.1631-1638
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• 2015

This paper describes the design methodology of the operator training simulator for power monitor and control system in the railway system. In power system, the purpose of energy management system was to monitor, control, and analyze the performance of generation and transmission system based on H/W and S/W. Network analysis applications provide a clear picture of power system characteristics using state estimation, power flow and short circuit analysis. In this respect, the operator training system in the railway system should be equipped with the methodology of these systems. First, the proposed database structure in the railway system was introduced. Then the overall structure of operator training system based on railway analysis applications was proposed. Finally, a methodology to verify the performance of the developed applications was described.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 추계학술대회 논문집 전력기술부문
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• pp.113-115
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• 2004

This year korea power system had recorded highest peak load for 6 times and finally it made new peak load 51,264MW at July 29th 3:00 PM. The new peak load is increased 8.2% from the last year peak load 47,385MW and korea power system entered 50,000MW load era. The Korea Power Exchange (KPX) snapped power system data at the peak load time using state estimation function in the EMS. And authors converted the power system data at peak load to PSS/E power flow format. Using this PSS/E peak load power flow data, this paper explains demand analysis result shun capacitor operation, voltage distribution at the peak load. And the paper shows the simulation result of 2 contingency analysis using the snapped PSS/E peak load data.

• 전력전자학회논문지
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• 제26권3호
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2016년도 추계학술대회 논문집
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• pp.59-60
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• 2016

Flooded lead acid batteries are still very popular in the industry because of their low cost as compared to their counterparts. State of Charge (SOC) estimation is of great importance for a flooded lead acid battery to ensure its safe working and to prevent it from over-charging or over-discharging. Different types of Kalman Filters are widely used for SOC estimation of batteries. The values of process and measurement noise covariance of a filter are usually calculated by trial and error method and taken as constant throughout the estimation process. While in practical cases, these values can vary as well depending upon the dynamics of the system. Therefore an Adaptive Unscented Kalman Filter (AUKF) is introduced in which the values of the process and measurement noise covariance are updated in each iteration based on the residual system error. A comparison of traditional and Adaptive Unscented Kalman Filter is presented in the paper. The results show that SOC estimation error by the proposed method is further reduced by 3 % as compared to traditional Unscented Kalman Filter.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(I)
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• pp.659-661
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• 1987

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.19-28
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• 2017

False data injection attacks have recently been introduced as one of important issues related to cyber-attacks on electric power grids. These attacks aim to compromise the readings of multiple power meters in order to mislead the operation and control centers. Recent studies have shown that if a malicious attacker has complete knowledge of the power grid topology and branch admittances, s/he can adjust the false data injection attack such that the attack remains undetected and successfully passes the bad data detection tests that are used in power system state estimation. In this paper, we investigate that a practical false data injection attack is essentially a cyber-attack with uncertain information due to the attackers lack of knowledge with respect to the power grid parameters because the attacker has limited physical access to electric facilities and limited resources to compromise meters. We mathematically formulated a method of identifying the most vulnerable locations to false data injection attack. Furthermore, we suggest minimum topology changes or phasor measurement units (PMUs) installation in the given power grids for mitigating such attacks and indicate a new security metrics that can compare different power grid topologies. The proposed metrics for performance is verified in standard IEEE 30-bus system. We show that the robustness of grids can be improved dramatically with minimum topology changes and low cost.