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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.


Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, a new probabilistic technique for fault classification using an adaptive Kalman filter using voltage measurements is described. But this technique assumes the features of a faulted phase while the other has features of an unfaulted phase and the condition of the phase, faulted or non-faulted, is then decided from the computed a posteriori probabilities.
Abstract: This paper describes a new probabilistic technique for fault classification to be used in digital distance protection of power systems. The new technique is based on an adaptive Kalman filter using voltage measurements. The voltage data of each phase is processed in two Kalman filter models simultaneously. One Kalman filter assumes the features of a faulted phase while the other has the features of an unfaulted phase. The condition of the phase, faulted or non-faulted, is then decided from the computed a posteriori probabilities.

39 citations

Proceedings ArticleDOI
28 Sep 2004
TL;DR: Extended Kalman and unscented Kalman filters are developed for multi-rate systems in the context of the SLAM problem and a performance index is introduced, showing that EKF gives better performance than UKF.
Abstract: In this paper, extended Kalman and unscented Kalman filters are developed for multi-rate systems in the context of the SLAM problem. These multi-rate filters have been extensively compared and tested with experimental data taken from a parking lot. Both multi-rate filters have improved the estimation with respect to the conventional single-rate Kalman filter. A performance index is introduced, showing that EKF gives better performance than UKF. In order to complete the SLAM solution, well-known techniques for feature extraction, data association and map building have been also implemented.

39 citations

Journal ArticleDOI
TL;DR: In this article, Park and Rizzoni (1993) obtained closed-form expressions for detection filters; the structure of all detection filters for a given fault direction was defined, and the necessary conditions for the existence of the optimal detection filter were obtained, and a numerical solution technique was shown to be feasible by virtue of the uniqueness of the detection filter gains.
Abstract: Park and Rizzoni (1993) obtained closed-form expressions for detection filters; i.e. the structure of all detection filters for a given fault direction was defined. An important consequence of these results is that they permit the formation of the optimal detection filter problem, for optimization with respect to process and measurement noises. The necessary conditions for the existence of the optimal detection filter are obtained, and a numerical solution technique is shown to be feasible by virtue of the uniqueness of the detection filter gains. From an optimization point of view the problem can be regarded as optimal estimation with some structural constraints on the observer gain. This problem is solved for both the continuous-time and the discrete-time cases.

39 citations

Journal ArticleDOI
TL;DR: In this paper, the derivation of Kalman filter for discrete time stochastic fractional system is investigated based on a novel cumulative vector form model for fractional systems, and the validity of the proposed method has been compared with a previously presented method via simulation results.

39 citations

Journal ArticleDOI
TL;DR: In this paper, an unscented Kalman filter with unknown input (UKF-UI) is proposed for structural health assessment, which does not need the information on the time history of the excitation to identify structural systems represented by finite elements and can identify defects in them using only a limited amount of noise-contaminated nonlinear response information.
Abstract: A novel procedure for structural health assessment, denoted as unscented Kalman filter with unknown input (UKF-UI), is proposed using the nonlinear system identification concept. To increase its implementation potential, a substructure concept is introduced, producing a two-stage approach. It integrates the unscented Kalman filter concept and an iterative least-squares technique. The two most important features of the method are that it does not need the information on the time history of the excitation to identify structural systems represented by finite elements, and that it can identify defects in them using only a limited amount of noise-contaminated nonlinear response information. The proposed method is robust enough to detect the locations and severity of defects at different locations in the structure. The defect detection capability increases significantly if the defective member is in the substructure or close to it. The method is conclusively verified with the help of two examples using ...

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202331
202277
20211
201910
201836
2017269