• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1469-1472
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• 1997

For system state estimation, existing LMS type esimators widely used. For example Kalman filter is one of them. In this paper, a state estimator is derived for the H$_{\infty}$ norm of the estimation error spectrum matrix to be minimized. For the solution of this problem classical NP interpolation problem is used. Also, by deriving the duality between the filter problem and the well-known H$_{\infty}$ control problem, another solution is obtained. The computer simuation results show that H$_{\infty}$ estimator has less estimation error and so this is better than the existing Kalman filter estimator.or.

• Smart Structures and Systems
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• v.25 no.1
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• pp.37-45
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• 2020

This paper aims at enlightening the properties, computational and implementation issues related to Kalman filter based state estimation algorithms and sliding mode observers, by applying them for estimating the states of a smart structure system. The Kalman based estimators considered in this work are Kalman filter and information filter and, the sliding mode observers considered are Utkin observer and higher order sliding mode observer. A fourth order linear time invariant model of a piezo actuated beam is used in this work. This structure is embedded with four number of piezo patches, of which two act as sensors, one as disturbance actuator and the other as control actuator. The performance of the state estimation algorithms is evaluated through simulation, for the first two vibrating modes of the piezo actuated structure, when the structure is maintained at first mode and second mode resonance.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.1
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• pp.171-178
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• 2017

This paper studies the state estimation problem for static neural networks with time-varying delay. Unlike other studies, the controller scheme, which involves time-varying sampling and uncertainties, is first employed to design the state estimator for delayed static neural networks. Based on Lyapunov functional approach and linear matrix inequality technique, the non-fragile sampled-data estimator is designed such that the resulting estimation error system is globally asymptotically stable with $H_{\infty}$ performance. Finally, the effectiveness of the developed results is demonstrated by a numerical example.

• Transactions on Electrical and Electronic Materials
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• v.18 no.3
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• pp.136-140
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• 2017

The state of charge (SOC) is an important parameter in a battery-management system (BMS), and is very significant for accurately estimating the SOC of a battery. Li-ion batteries boast of excellent performance, and can only remain at their best working state by means of accurate SOC estimation that gives full play to their performances and raises their economic benefits. This paper summarizes some measures taken in SOC estimation, including the discharge experiment method, the ampere-hour integral method, the open circuit voltage method, the Kalman filter method, the neural network method, and electrochemical impedance spectroscopy (EIS. The principles of the various SOC estimation methods are introduced, and their advantages and disadvantages, as well as the working conditions adopted during these methods, are discussed and analyzed.

• Proceedings of the KIEE Conference
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• 1997.07c
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.18-23
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• 1993

In this paper, we attempt to estimate the state of a finite state system. In such system, we can observe time series data which has some significant behaviors corresponding to its system states. The behavior is characterized by feature parameters extracted from time series. Our thought is that the system output time series data is expressed as a sequence of behavior patterns which are represented by clusters in feature parameters space. An algorithm jointing fuzzy clustering to fuzzy finite state transition model is suggested.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.31.1-31.1
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• 2011

In this study, we investigated alignment state estimation performances of the three methods i.e. merit function regression (MFR), differential wavefront sampling (DWS) and Multiple Design Configuration Optimization (MDCO). The three target optical systems are 1) a two-mirror Cassegrain system for deep space Earth observation, 2) intermediate size three-mirror anastigmat for Earth ocean monitoring, and 3) extremely large segmented optical system for astronomical observation. We ran alignment state estimation simulation for several alignment perturbation cases including 1mm to 10mm in decenter and from 0.1 to 1 degree in tilt perturbation error for the two-mirror Cassegrain system. In general, we note that MDCO shows more competitive estimation performance than MFR and DWS. The computational concept, case definition and the simulation results are discussed with implications to future works.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• Proceedings of the KIEE Conference
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• 1988.07a
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• pp.841-842
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• 1988

For determination of black-out area and restoration area by an expert system for fault section estimation and power system restoration using information from circuit breakers, it is necessary that the recognition of system isolation state and a method of finding the change of system isolation state by the state transition of breakers in isolated system. This paper presents a method of resolving the above problem by rule-based approach.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.3
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• pp.103-109
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• 2003

The validity of measured data is fundamental factor for the power system automation Measured values could have errors that are caused by the communication errors and malfunctioning measuring devices. The accuracy and reliability of measured values at a substation is an important condition for robust and fault tolerant automata. Errors can be reduced by state estimation, however, global reliability of state estimation goes down in case of the existence of some bad data In this paper, a least square state estimation and bad sensor detection algorithm based on chi-square theory, ale proposed and it is applied to a domestic 154kV distribution substations. A simulator together with user friendly graphic users interface is developed using C language and Visual Basic. TCP/IP is equipped for future connection with other operation systems.