scispace - formally typeset
Search or ask a question
Topic

Invariant extended Kalman filter

About: Invariant extended Kalman filter is a research topic. Over the lifetime, 7079 publications have been published within this topic receiving 187702 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, two techniques for optimal tracking of power system voltage phasors and frequency deviation were presented, one based on a two-state linear Kalman filter model and the other based on three-state extended Kalman filters.
Abstract: This paper presents two techniques for optimal tracking of power system voltage phasors and frequency deviation. The first technique is based on a two-state linear Kalman filter model. The second technique is based on a three-state extended Kalman filter model. In the latter the frequency deviation is considered a third state variable and is recursively computed on-line. It is shown that the Kalman filter models are well suited for noisy measurements. The effect of sampling rate, computer burden and overall accuracy are also investigated. Finally comparison with other techniques is presented.

203 citations

Journal ArticleDOI
TL;DR: In this paper, an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking is proposed, which models the system as a linear frequency independent state model and does not require an exact knowledge of the noise covariance matrix.
Abstract: Knowledge of the process noise covariance matrix Q is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of Q for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking. The method models the system as a linear frequency independent state model and does not require an exact knowledge of the noise covariance matrix Q. As an alternative, the proposed adaptive Kalman filter switches between the two basic Q models for steady-state and transient estimation. Its adaptive function allows for the resetting of the Kalman gain to avoid Kalman filter divergence problems under steady-state and allow fast tracking of system variations in transient conditions. Simulation results on the 220 kV network of the lower South Island of New Zealand are presented to validate this approach.

203 citations

Journal ArticleDOI
TL;DR: It is shown that the suggested filter possesses the unbiasedness property and the remarkable deadbeat property irrespective of any horizon initial condition.
Abstract: A receding horizon Kalman FIR filter is presented that combines the Kalman filter and the receding horizon strategy when the horizon initial state is assumed to be unknown. The suggested filter is a FIR filter form which has many good inherent properties. It can always be defined irrespective of singularity problems caused by unknown information about the horizon initial state. The suggested filter can be represented in either an iterative form or a standard FIR form. It is also shown that the suggested filter possesses the unbiasedness property and the remarkable deadbeat property irrespective of any horizon initial condition. The validity of the suggested filter is illustrated by numerical examples.

202 citations

Journal ArticleDOI
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

Journal ArticleDOI
TL;DR: In this article, an EKF-UI approach with unknown inputs (excitations) is proposed to identify the structural parameters, such as the stiffness, damping and other nonlinear parameters, as well as the unmeasured excitations.
Abstract: After a major event, such as a strong earthquake, a rapid assessment of the state (or damage) of the structure, including buildings, bridges and others, is important for post-event emergency responses, rescues and management. Time domain analysis methodologies based on measured vibration data, such as the least squares estimation and the extended Kalman filter (EKF), have been studied and shown to be useful for the on-line tracking of structural damages. The traditional EKF method requires that all the external excitation data (input data) be measured or available, which may not be the case for many structures. In this paper, an EKF approach with unknown inputs (excitations), referred to as EKF-UI, is proposed to identify the structural parameters, such as the stiffness, damping and other nonlinear parameters, as well as the unmeasured excitations. Analytical solution for the proposed EKF-UI approach is derived and presented. Such an analytical solution for EKF-UI is not available in the previous literature. An adaptive tracking technique recently developed is also implemented in the proposed EKF-UI approach to track the variations of structural parameters due to damages. Simulation results for linear and nonlinear structures demonstrate that the proposed approach is capable of identifying the structural parameters, their variations due to damages, and unknown excitations. Copyright © 2006 John Wiley & Sons, Ltd.

201 citations


Network Information
Related Topics (5)
Control theory
299.6K papers, 3.1M citations
91% related
Robustness (computer science)
94.7K papers, 1.6M citations
89% related
Artificial neural network
207K papers, 4.5M citations
85% related
Support vector machine
73.6K papers, 1.7M citations
84% related
Optimization problem
96.4K papers, 2.1M citations
84% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202348
2022162
202120
20208
201914
201851