• KIEE International Transactions on Power Engineering
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• v.2A no.3
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• pp.109-113
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• 2002

This paper addresses the issue of parameter error identification and estimation in electric power systems. Parameter error identification and estimation is carried out as a part of the state estimation. A two stage estimation procedure is used to detect and identify parameter errors. Suspected parameters are identified by the WLAV state estimator in the first stage. A new WLAV state estimator adding suspected system parameters in the state vector is used to estimate the exact values of parameters. Supporting examples are given by using the IEEE 14 bus system.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.4
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• pp.332-338
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• 2022

This study proposes a battery state-of-health estimation method by applying a feature extraction technique. The technique that can improve estimation performance is the process of identifying and extracting meaningful data. To apply a data-driven-based aging state estimation method to batteries, health indicators are used as training data. However, limitations occur in extracting health indicators from charge/discharge cycles. This study proposes a deep-learning-based battery state-of-health estimation method that applies feature extraction techniques to compensate for this problem. According to the performance evaluation result of the proposed method, it has a low estimation error of 0.3887% based on an absolute error evaluation method.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.2
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• pp.84-91
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• 2014

This paper introduces the application of supervisory monitoring system for substation automation based on IEC 61850. The objective of proposed system is detection of such a malfunction or degradation of devices. The supervisory monitoring procedure consists of a two step - topology processor and state estimation. The topology processor using artificial intelligence is a preprocessing step of state estimation. Topology processor identifies the topology structure of switches in substation and detects an error of ON/OFF state data. The state estimation is an algorithm that minimizes an error between optimal estimation values and real values. The proposed system is applied to standard digital substation based on IEC 61850 for performance verification.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.9
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• pp.451-458
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• 2000

This paper addresses the issues of the parameter error detection and identification in electric power systems. In this paper, the parameter error identification and estimation is carried out as part of the state estimation. A two stage estimation procedure is used to detect and identify the parameter errors. The suspected parameters are identified by the WLAV state estimator as the first stage. A new WLAV state estimator adding the suspected system parameters in the state vector is used to estimate the exact value of parameter errors. Supporting examples are given by using IEEE 14 bus system.

• Journal of the Korean Institute of Navigation
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• v.17 no.1
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• pp.91-98
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• 1993

This paper presents a class of Bayes estimation of component steady-state availability . Throughout this paper, we will denote the mean time between failure and the mean time between repair by MTBF and MTBR respectively. In section 2 , we investigated Bayes estimation of the steady-state availability for noninformative prior density function and in section 3, we compute Bayes estimation for conjugate prior density function.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.10 no.1
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• pp.27-32
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• 2011

These days more electric devices are implemented in car, and more accurate estimation of SoC is required. OCV with current integration and Internal Resistance is essential method of Battery SoC Estimation. In this paper we propose OCV with current integration method and compare with Internal Resistance method. In OCV with current integration method estimation error was less than average 2%, but requires more than 5 minutes to stabilize OCV. If Stop and Running conditions are change frequently, estimation error will increase. In Internal resistance Modeling method, in high SoC state, estimation error was more than 15%, and in low SoC state, estimation error was less than 8%.

• Korean Journal of Remote Sensing
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• v.7 no.1
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• pp.45-67
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• 1991

An overview is given on stochastic techniques with which remotely sensed data may be used together with ground measurements for purposes of state estimation and prediction. They can explicitly account for spatiotemporal differences in measurement characteristics between ground measurements and remotely sensed data, and are suitable for highly variant space or space-time processes, such as atmosperic processes, which may be viewed as (containing) a random process. For state estimation of static ststems, optimal linear estimation is described. As alternatives, various co-kriging estimation techniques are also described, including simple, ordinary, universal, lognormal, disjunctive, indicator, and Bayesian extersion to simple and lognormal. For illustrative purposes, very simple examples of optimal linear estimation and simple co-kriging are given. For state estimation and prediction of dynamic system, distributed-parameter kalman filter is described. Issues concerning actual implemention are given, and with application potential are described.

• Journal of Advanced Information Technology and Convergence
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• v.6 no.1
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• pp.45-57
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• 2016

This paper proposes an adaptive channel estimation scheme for OFDM-based IEEE 802.11ad wireless local area network (WLAN). The standard supports two types of information of OFDM packets for estimating the communication channels, which are the channel estimation field (CEF) of preamble and pilot subcarriers. The CEF-based channel estimation provides better BER (bit error rate) performance at slow fading channel state, whereas the pilot-based channel estimation is good at fast fading channel state. Hence, a combined channel estimation method is introduced to improve the performance. The prediction of the channel state to select the proper channel estimation method is required. In this work, an adaptive channel estimation scheme is also proposed to improve the performance of channel estimation (CE). Basing on a channel quality indicator (CQI), the proper channel estimation method corresponding to the channel type is decided.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.1
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• pp.29-36
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• 2002

It is required that the current state of system should be Precisely monitored for efficient and safe operation of the subway power system and it is an important Problem to secure the high quality data for state estimation. The current state of subway power system is estimated by using data transmitted to control center from every measuring instrument. The high accuracy and trust can be maintained if the measured data have a high quality. But it is difficult to estimate the accurate state of system because of the noises in transmitted data and the inaccuracy of measuring instruments. So the object is to reduce the difference between the real values and the measured values in order to improve considerably the inaccuracy due to Instrumental errors and transmission noises using the state estimation method. In this paper, we proposes a new state estimation to estimate the accurate state of the subway power system from the measured values of a Sang-In station in Daegu subway and consider the possibility of application to the real subway power system. on the basis of that. The simulation results show to make sure of the possibility to apply to the real system usefully.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.3
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• pp.285-294
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• 2012

Using bearing measurement only, target motion state is not directly obtained so that TMA (Target Motion Analysis) is needed for this situation. TMA is a nonlinear estimation technique used in passive SONAR systems. Also it is the one of important techniques for underwater combat management systems. TMA can be divided to two parts: batch estimation and sequential estimation. It is preferable to use sequential estimation for reducing computational load as well as adaptively to target maneuvers, batch estimation is still required to attain target initial state vector for convergence of sequential estimation. Selection of batch time interval which depends on observability is critical in TMA performance. Batch estimation in general utilizes predetermined batch time interval. In this paper, we propose a new method called the BTIS (Batch Time Interval and Initial State Estimation). The proposed BTIS estimates target initial status and determines the batch time interval sequentially by using a bank of GMM-TS (Gaussian Mixture Measurement-Track Splitting) filters. The performance of the proposal method is verified by a Monte Carlo simulation study.