• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2538-2541
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• 2005

This paper deals one of the methods of system identification, especially on-line system identification in time-domain. The algorithm in this study needs all states of the system as well input to it for system identification. In this reason, Kalman filter is used for state estimation. But in order to implement a state estimator, the fact that a system model must be known is logical contradiction. To overcome this, state estimation and system parameter estimation are performed simultaneously in one sample. And the result of the system parameter estimation is used as basis to state estimation in next sample. On-line system identification comes, in every sample by performing both processes of state estimation and parameter estimation that are related mutually and recursively. This paper demonstrates the validity of proposed algorithm through an example of an unstable inverted pendulum system. This algorithm can be useful for on-line system identification of a system that has fewer number of measurable output than system order or number of states.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.34 no.5
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• pp.201-209
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• 1985

This paper describes a method for the state estimation by a modified fast decoupled estimation method and system decomposition. The state values are gained by using the weighted least square estimation method, fast decoupled estimation method, and modified fast decoupled estimation method. The estimated values of each method were compared about effectiveness of state values, respectively. This paper investigated the effects of impedance of well-condition or ill-condition into lines. The characteristics of state estimation were gained through hierarchical state estimation. Each method was applied to three model power systems, and, the results of test for the proposed method are given.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1049-1053
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• 1999

It is important to measuring and monitoring about state vectors of power system for precise operation control. All state vectors cannot be measured because it is economically disadvantageous, so that some state vectors are determined using state estimator. Determination of observability is a important precondition of power system state estimation because state estimation can be performed when given power system is observable. Recently as time-synchronization technique progress, using the PMU(Phasor Measurement Unit), state vector can be measured directly so that voltage phasor and current phasor measurements can be used for power system estimation. In this paper, observability algorithm is proposed to determinate the observability with real/reactive injection power measurements and real/reactive lineflow power measurements of existing measurement system and with phasor measurements of PMU. The jacobian matrix is newly composed for state estimation with measurements of added PMU, and state estimation is performed with least square estimatior. Comparison between state estimation result of existing measurement system and that of measurement system added PMU is presented.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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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.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.05a
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• pp.117-120
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• 2002

Electrical power system has very complexity problem that it is plan measurement system to achieve Harmonic State Estimation (HSE). This complexity problem depends on discord of necessary accuracy, certainty of noise that exist in data communication damage and converter, adaptability of network modification and minimum of expense size of system, estimated monitering. Also, quantity of available measurement equipment for harmonic measurement has been limited. Therefore, systematic method that choose measurement location for harmonic state estimation. This paper is that see proposed HSE that use Observability Analysis(OA) for harmonic state estimation of electrical power system. OA depends on measurement number, measurement location and measurement form here, it is analysis method that depend on network form and admittance of the system. OA used achieve harmonic state estimation that it is Applied to New Zealand electrical power system to prove validity of HSE algorithm that propose. This study result about harmonic state estimation of electrical power system displayed very economical and effective method by OA.

• 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 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
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• v.35 no.4
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• pp.148-155
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• 1986

This paper presents a new decoupled power system state estimation method. The decoupling is achieved via simple linear transformation on power measurements in contrast with the modified fast decoupled state estimation method which assumes decoupling by direct negligence of the off-diagonal blocks of the observation functions. The new estimation method is compared with the modified decoupled state estimation method against IEEE-14 bus model power system and 25 bus model power system in several system conditions. It is observed that the proposed method shows better convergence performance and filtering performance than a modified fast decoupled state estimation.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.588-598
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• 2020

An alternative state estimation filtering algorithm is designed for continuous time systems with noises as well as control input. Two kinds of estimation filters, which have different measurement memory structures, are operated selectively in order to use both filters effectively as needed. Firstly, the estimation filter with infinite memory structure is operated for a certain continuous time system. Secondly, the estimation filter with finite memory structure is operated for temporarily uncertain continuous time system. That is, depending on the presence of uncertainty, one of infinite memory structure and finite memory structure filtered estimates is operated selectively to obtain the valid estimate. A couple of test variables and declaration rule are developed to detect uncertainty presence or uncertainty absence, to operate the suitable one from two kinds of filtered estimates, and to obtain ultimately the valid filtered estimate. Through computer simulations for a continuous time aircraft engine system with different measurement memory lengths and temporary model uncertainties, the proposed state estimation filtering algorithm can work well in temporarily uncertain as well as certain continuous time systems. Moreover, the proposed state estimation filtering algorithm shows remarkable superiority to the infinite memory structure filtering when temporary uncertainties occur in succession.