Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/71008

Robust Adaptive Nonlinear Kalman Filter for Synchronous Machine Parameter Calibration

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Title:Robust Adaptive Nonlinear Kalman Filter for Synchronous Machine Parameter Calibration
Authors:Zhao, Junbo
Wang, Shaobu
Huang, Renke
Fan, Rui
Xu, Yijun
show 1 moreHuang, Zhenyu
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Keywords:Monitoring, Control and Protection
dynamic state estimation
kalman filter
noise
parameter calibration
show 1 moresynchrophasor measurements
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Date Issued:05 Jan 2021
Abstract:This paper proposes a robust and adaptive nonlinear Kalman filter for synchronous machine parameter calibration. The key idea is to develop the polynomial chaos-based analysis of variance (ANOVA) method for suspicious parameter detection. ANOVA allows us to derive a set of adaptive weights that can be used to address local parameter optimality issue when performing joint state and parameter estimation. It is shown that if erroneous parameters have strong correlations, the widely used methods that augment state and parameter for joint estimation will lead to large biases. By contrast, thanks to the derived adaptive weights for the suspicious parameters, the proposed method can effectively deal with the parameter dependence, yielding much better calibration results. In addition, the robustness of the proposed method enables us to filter non-Gaussian noise. Simulations carried out on the IEEE 39-bus system validate the effectiveness and robustness of the proposed approach.
Pages/Duration:8 pages
URI:http://hdl.handle.net/10125/71008
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.393
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Monitoring, Control and Protection


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