• 제목/요약/키워드: optimal state estimation

검색결과 183건 처리시간 0.032초

고조파 상태 추정에 있어서 유전 알고리즘을 이용한 최적 측정위치 선정 (Optimal Placement of Measurements using Genetic Algorithms for Harmonic State Estimation)

  • 정형환;왕용필;이정필;박희철
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2002년도 하계학술대회 논문집 A
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    • pp.298-300
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    • 2002
  • The design of a measurement system to perform Harmonic State Estimation (HSE) is a very complex problem. In particular, the number of available harmonic instruments(Continuous Harmonic Analysis in Real Time : CHART) 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). This HSE has been applied to the New Zealand AC 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 GAs in the HSE.

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해상 정보 취득과 연료 소모량 추정을 기반으로 한 선박 경제 운항 경로 결정 방법 (A Method for Determining a Ship Economic Route based on the Acquisition of the Sea State and Estimation of Fuel Consumption)

  • 노명일
    • 한국CDE학회논문집
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    • 제17권2호
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    • pp.71-78
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    • 2012
  • Recently, the ratio of fuel cost to the operational cost of a ship is increasing according to the increase of the international oil price. Thus, many studies are being made to reduce the cost; for example, a method for acquiring the sea state information, a method for estimating fuel consumption, a method for determining the ship's optimal route, and so on. However, these studies were not incorporated together and being independently made in different fields. In this study, by improving and incorporating such studies, a method for determining a ship economic route based on the acquisition of the sea state and estimation of fuel consumption was proposed. To evaluate the applicability of the proposed method, it was applied to an optimal routing problem of the ocean area including many islands. The result shows that the proposed method can yield the economic route minimizing fuel consumption.

스마트 마이크로그리드 실시간 상태 추정에 관한 연구 (A Study on Real-time State Estimation for Smart Microgrids)

  • 배준형;이상우;박태준;이동하;강진규
    • 한국태양에너지학회:학술대회논문집
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    • 한국태양에너지학회 2012년도 춘계학술발표대회 논문집
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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.

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이산시간 무편향 선형 최적 유한구간 필터 (Discrete-time BLUFIR filter)

  • 박상환;권욱현;권오규
    • 제어로봇시스템학회:학술대회논문집
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    • 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
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    • pp.980-983
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    • 1996
  • A new version of the discrete-time optimal FIR (finite impulse response) filter utilizing only the measurements of finite sliding estimation window is suggested for linear time-invariant state-space models. This filter is called the BLUFIR (best linear unbiased finite impulse response) filter since it provides the BLUE (best linear unbiased estimate) of the state obtained from the measurements of the estimation window. It is shown that the BLUFIR filter has the deadbeat property when there are no noises in the estimation window.

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Segment Training Based Individual Channel Estimation for Multi-pair Two-Way Relay Network with Power Allocation

  • He, Xiandeng;Zhou, Ronghua;Chen, Nan;Zhang, Shun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제12권2호
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    • pp.566-578
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    • 2018
  • In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones.

Wireless sensor network design for large-scale infrastructures health monitoring with optimal information-lifespan tradeoff

  • Xiao-Han, Hao;Sin-Chi, Kuok;Ka-Veng, Yuen
    • Smart Structures and Systems
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    • 제30권6호
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    • pp.583-599
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    • 2022
  • In this paper, a multi-objective wireless sensor network configuration optimization method is proposed. The proposed method aims to determine the optimal information and lifespan wireless sensor network for structural health monitoring of large-scale infrastructures. In particular, cluster-based wireless sensor networks with multi-type of sensors are considered. To optimize the lifetime of the wireless sensor network, a cluster-based network optimization algorithm that optimizes the arrangement of cluster heads and base station is developed. On the other hand, based on the Bayesian inference, the uncertainty of the estimated parameters can be quantified. The coefficient of variance of the estimated parameters can be obtained, which is utilized as a holistic measure to evaluate the estimation accuracy of sensor configurations with multi-type of sensors. The proposed method provides the optimal wireless sensor network configuration that satisfies the required estimation accuracy with the longest lifetime. The proposed method is illustrated by designing the optimal wireless sensor network configuration of a cable-stayed bridge and a space truss.

유전자 알고리즘을 이용한 제어파라미터 추정모드기반 HFC (Hybrid Fuzzy Controller Based on Control Parameter Estimation Mode Using Genetic Algorithms)

  • 이대근;오성권;장성환
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 2000년도 하계학술대회 논문집 D
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    • pp.2545-2547
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    • 2000
  • In this paper, a hybrid fuzzy controller using genetic algorithm based on parameter estimation mode to obtain optimal control parameter is presented. First, The control input for the system in the HFC is a convex combination of the FLC's output in transient state and PID's output in steady state by a fuzzy variable, namely, membership function of weighting coefficient. Second, genetic algorithms is presented to automatically improve the performance of hybrid fuzzy controller utilizing the conventional methods for finding PID parameters and estimation mode of scaling factor. The algorithms estimates automatically the optimal values of scaling factors, PID parameters and membership function parameters of fuzzy control rules according to the rate of change and limitation condition of control input. Computer simulations are conducted to evaluate the performance of proposed hybrid fuzzy controller. ITAE, overshoot and rising time are used as a performance index of controller.

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Survey of nonlinear state estimation in aerospace systems with Gaussian priors

  • Coelho, Milca F.;Bousson, Kouamana;Ahmed, Kawser
    • Advances in aircraft and spacecraft science
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    • 제7권6호
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    • pp.495-516
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    • 2020
  • Nonlinear state estimation is a desirable and required technique for many situations in engineering (e.g., aircraft/spacecraft tracking, space situational awareness, collision warning, radar tracking, etc.). Due to high standards on performance in these applications, in the last few decades, there was an increasing demand for methods that are able to provide more accurate results. However, because of the mathematical complexity introduced by the nonlinearities of the models, the nonlinear state estimation uses techniques that, in practice, are not so well-established which, leads to sub-optimal results. It is important to take into account that each method will have advantages and limitations when facing specific environments. The main objective of this paper is to provide a comprehensive overview and interpretation of the most well-known methods for nonlinear state estimation with Gaussian priors. In particular, the Kalman filtering methods: EKF (Extended Kalman Filter), UKF (Unscented Kalman Filter), CKF (Cubature Kalman Filter) and EnKF (Ensemble Kalman Filter) with an aerospace perspective.

ESTIMATION OF NON-INTEGRAL AND INTEGRAL QUADRATIC FUNCTIONS IN LINEAR STOCHASTIC DIFFERENTIAL SYSTEMS

  • Song, IL Young;Shin, Vladimir;Choi, Won
    • Korean Journal of Mathematics
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    • 제25권1호
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    • pp.45-60
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    • 2017
  • This paper focuses on estimation of an non-integral quadratic function (NIQF) and integral quadratic function (IQF) of a random signal in dynamic system described by a linear stochastic differential equation. The quadratic form of an unobservable signal indicates useful information of a signal for control. The optimal (in mean square sense) and suboptimal estimates of NIQF and IQF represent a function of the Kalman estimate and its error covariance. The proposed estimation algorithms have a closed-form estimation procedure. The obtained estimates are studied in detail, including derivation of the exact formulas and differential equations for mean square errors. The results we demonstrate on practical example of a power of signal, and comparison analysis between optimal and suboptimal estimators is presented.

Online estimation of noise parameters for Kalman filter

  • Yuen, Ka-Veng;Liang, Peng-Fei;Kuok, Sin-Chi
    • Structural Engineering and Mechanics
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    • 제47권3호
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    • pp.361-381
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    • 2013
  • A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.