Topic
Alpha beta filter
About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.
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TL;DR: In this paper it is shown that a wise parametrization of the extended Kalman frequency tracker is characterized by just one parameter: the /spl epsi/ must be set to zero to achieve the basic property of unbiasedness in a noise-free setting.
Abstract: The problem of estimating the frequency of a harmonic signal embedded in broad-band noise is considered. The paper focuses on the extended Kalman filter frequency tracker, which is the application of the extended Kalman filter (EKF) framework to the frequency estimation problem. The EKF frequency tracker recently proposed in the literature is characterized by a vector of three design parameters {q,r,/spl epsi/}, whose role and tuning is still a controversial and unclear issue. In this paper it is shown that a wise parametrization of the extended Kalman frequency tracker is characterized by just one parameter: the /spl epsi/ must be set to zero to achieve the basic property of unbiasedness in a noise-free setting; the performances of the tracker are not influenced independently by q and r; and what really matters is the ratio /spl lambda/=r/q only. The proposed simplification of the extended Kalman filter frequency tracker allows an easier and more transparent tuning of its tracking behavior.
201 citations
TL;DR: This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data and makes some recommendations for the proper use of the methods.
Abstract: Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.
197 citations
Proceedings Article•
01 Aug 2007
TL;DR: In this article, the feasibility of applying Kalman filtering techniques to include dynamic state variables in the state estimation process is investigated, and the proposed Kalman filter based dynamic state estimation is tested on a multi-machine system with both large and small disturbances.
Abstract: The lack of dynamic information in the operation of power systems can be attributed to the use of steady state estimators, which generate the input values for many operational tools. This paper investigates the feasibility of applying Kalman Filtering techniques to include dynamic state variables in the state estimation process. The proposed Kalman Filter based dynamic state estimation is tested on a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance with respect to sampling rate and noise level are presented as well. The study results show that there is a promising path forward for the implementation of Kalman Filter based dynamic state estimation in conjunction with the emerging phasor measurement technologies.
188 citations
TL;DR: In this article, the application of stochastic state estimators in vehicle dynamics control is discussed, where it is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured.
Abstract: This paper deals with the application of stochastic state estimators in vehicle dynamics control. It is often unrealistic to assume that all vehicle states and the disturbances acting on it can be measured. System states that cannot be measured directly, can be estimated by a Kalman Filter. The idea of the Kalman filter is to implement a model of the real system in an on-board computer in parallel with the system itself. This paper will give 3 examples of this principle applied to automotive systems.
187 citations
TL;DR: In this paper, the Ensemble Kalman Filter (EnKF) was used for automatic history matching of reservoir characterization through a Monte Carlo method, in which an ensemble of reservoir models was used to estimate the correlation between reservoir response and reservoir variables.
Abstract: The problem of reservoir characterization through automatic history matching has been extensively studied in recent years Efficient applications have, however, required either an adjoint or a gradient simulator method to compute the gradient of the objective function or a sensitivity coefficient matrix for the minimization Both computations are expensive when the number of model parameters or the number of observation data is large The codes for gradient-based history matching methods are also complex and time-consuming to write This paper reports the use of the Ensemble Kalman Filter (EnKF) for automatic history matching EnKF is a Monte Carlo method, in which an ensemble of reservoir models is used The correlation between reservoir response (eg watercut and rate) and reservoir variables (eg permeability and porosity) can be estimated from the ensemble An estimate of uncertainty in future reservoir performance can also be obtained from the ensemble The methodology of EnKF consists of a forecast step and an assimilation step A finite-difference, 3-D, 3-phase black-oil reservoir simulator is used for stepping forward the reservoir states However, unlike the traditional history matching, the source code of the reservoir simulator is not required, which allows this method to be used with any reservoir simulator Moreover, this forward step is well suited for parallel computation since the time evolution of ensemble reservoir models are independent, hence the ensemble of reservoir models can be advanced in time simultaneously using multiple processors Only the data assimilation step, ie the computation of Kalman filter, requires communication between processors
184 citations