• Title/Summary/Keyword: optimal state estimation

Search Result 183, Processing Time 0.028 seconds

Unknown Input Estimation using the Optimal FIR Smoother (최적 유한 임펄스 응답 평활기를 이용한 미지 입력 추정 기법)

  • Kwon, Bo-Kyu
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.20 no.2
    • /
    • pp.170-174
    • /
    • 2014
  • In this paper, an unknown input estimation method via the optimal FIR smoother is proposed for linear discrete-time systems. The unknown inputs are represented by random walk processes and treated as auxiliary states in augmented state space models. In order to estimate augmented states which include unknown inputs, the optimal FIR smoother is applied to the augmented state space model. Since the optimal FIR smoother is unbiased and independent of any a priori information of the augmented state, the estimates of each unknown input are independent of the initial state and of other unknown inputs. Moreover, the proposed method can be applied to stochastic singular systems, since the optimal FIR smoother is derived without the assumption that the system matrix is nonsingular. A numerical example is given to show the performance of the proposed estimation method.

Optimal Measurement Placement for Static Harmonic State Estimation in the Power Systems based on Genetic Algorithm

  • Dehkordl, Behzad Mirzaeian;Fesharaki, Fariborz Haghighatdar;Kiyournarsi, Arash
    • Journal of Electrical Engineering and Technology
    • /
    • v.4 no.2
    • /
    • pp.175-184
    • /
    • 2009
  • In this paper, a method for optimal measurement placement in the problem of static harmonic state estimation in power systems is proposed. At first, for achieving to a suitable method by considering the precision factor of the estimation, a procedure based on Genetic Algorithm (GA) for optimal placement is suggested. Optimal placement by regarding the precision factor has an evident solution, and the proposed method is successful in achieving the mentioned solution. But, the previous applied method, which is called the Sequential Elimination (SE) algorithm, can not achieve to the evident solution of the mentioned problem. Finally, considering both precision and economic factors together in solving the optimal placement problem, a practical method based on GA is proposed. The simulation results are shown an improvement in the precision of the estimation by using the proposed method.

State estimation of stochastic bilinear system (추계 이선형 시스템의 상태추정)

  • 황춘식
    • 전기의세계
    • /
    • v.30 no.11
    • /
    • pp.728-733
    • /
    • 1981
  • Most of real world systems are highly non-linear. But due to difficulties in analyzing and dealing with it, only the linear system theory is well estabilished. Bilinear system where state and control are linear but not linear jointly is introduced. Here shows that optimal state estimation of stochastic bilinear system requirs infinite dimensional filter, thus onesub-optimal estimator for this system is suggested.

  • PDF

Application of Immune Algorithm for Harmonic State Estimation (전력시스템 고조파 상태 추정에서 면역 알고리즘 적용)

  • Wang Yong-Peel;Park In-Pyo;Chung Hyeng-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.53 no.12
    • /
    • pp.645-654
    • /
    • 2004
  • The design of a measurement system to perform Harmonic State Estimation(HSE) is a very complex problem. In particular, the number of available harmonic analysis measurement instruments is always limited. Therefore, a systematic procedure is needed to design the optimal placement of measurement points. This paper presents an optimal algorithm of HSE which is based on an optimal placement of measurement points using Immune Algorithm (IAs). This IA-HSE has been applied to power system for the validation of an optimal algorithm of HSE. The study results have indicated an economical and effective method for optimal placement of measurement points using Immune Algorithm (IAs) in the HSE.

A Finite Impulse Response Fixed-lag Smoother for Discrete-time Nonlinear Systems (이산 비선형 시스템에 대한 유한 임펄스 응답 고정 시간 지연 평활기)

  • Kwon, Bo-Kyu;Han, Sekyung;Han, Soohee
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.21 no.9
    • /
    • pp.807-810
    • /
    • 2015
  • In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

Power System State Estimation Using Parallel PSO Algorithm based on PC cluster (PC 클러스터 기반 병렬 PSO 알고리즘을 이용한 전력계통의 상태추정)

  • Jeong, Hee-Myung;Park, June-Ho;Lee, Hwa-Seok
    • Proceedings of the KIEE Conference
    • /
    • 2008.07a
    • /
    • pp.303-304
    • /
    • 2008
  • For the state estimation problem, the weighted least squares (WLS) method and the fast decoupled method are widely used at present. However, these algorithms can converge to local optimal solutions. Recently, modern heuristic optimization methods such as Particle Swarm Optimization (PSO) have been introduced to overcome the disadvantage of the classical optimization problem. However, heuristic optimization methods based on populations require a lengthy computing time to find an optimal solution. In this paper, we used PSO to search for the optimal solution of state estimation in power systems. To overcome the shortcoming of heuristic optimization methods, we proposed parallel processing of the PSO algorithm based on the PC cluster system. the proposed approach was tested with the IEEE-118 bus systems. From the simulation results, we found that the parallel PSO based on the PC cluster system can be applicable for power system state estimation.

  • PDF

On State Estimation Using Remotely Sensed Data and Ground Measurements -An Overview of Some Useful Tools-

  • Seo, Dong-Jun
    • Korean Journal of Remote Sensing
    • /
    • v.7 no.1
    • /
    • pp.45-67
    • /
    • 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.

Optimal Placement of Measurement Using GAs in Harmonic State Estimation of Power System (전력시스템 고조파 상태 춘정에서 GA를 미용한 최적 측정위치 선정)

  • 정형환;왕용필;박희철;안병철
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.52 no.8
    • /
    • pp.471-480
    • /
    • 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).

A Study on Power System State Estimation and bad data detection Using PSO (PSO기법을 이용한 전력계통의 상태추정해법과 불량정보처리에 관한 연구)

  • Ryu, Seung-Oh;Jeong, Hee-Myung;Park, June-Ho;Lee, Hwa-Seok
    • Proceedings of the KIEE Conference
    • /
    • 2008.11a
    • /
    • pp.261-263
    • /
    • 2008
  • In power systems operation, state estimation takes an important role in security control. For the state estimation problem, the weighted least squares(WLS) method and the fast decoupled method have been widely used at present. But these algorithms have disadvantage of converging local optimal solution. In these days, a modern heuristic optimization method such as Particle Swarm Optimization(PSO), are introduced to overcome the problems of classical optimization. In this paper, we proposed particle swarm optimization (PSO) to search an optimal solution of state estimation in power systems. To demonstrate the usefulness of the proposed method, PSO algorithm was tested in the IEEE-57 bus systems. From the simulation results, we can find that the PSO algorithm is applicable for power system state estimation.

  • PDF

An Optimal Fixed-lag FIR Smoother for Discrete Time-varying State Space Models (이산 시변 상태공간 모델을 위한 최적 고정 시간 지연 FIR 평활기)

  • Kwon, Bo-Kyu;Han, Soohee
    • Journal of Institute of Control, Robotics and Systems
    • /
    • v.20 no.1
    • /
    • pp.1-5
    • /
    • 2014
  • In this paper, we propose an optimal fixed-lag FIR (Finite-Impulse-Response) smoother for a class of discrete time-varying state-space signal models. The proposed fixed-lag FIR smoother is linear with respect to inputs and outputs on the recent finite horizon and estimates the delayed state so that the variance of the estimation error is minimized with the unbiased constraint. Since the proposed smoother is derived with system inputs, it can be adapted to feedback control system. Additionally, the proposed smoother can give more general solution than the optimal FIR filter, because it reduced to the optimal FIR filter by setting the fixed-lag size as zero. A numerical example is presented to illustrate the performance of the proposed smoother by comparing with an optimal FIR filter and a conventional fixed-lag Kalman smoother.