A nonparametric denoising approach for thevenin equivalent parameters estimation based on taut-string-multiresolution algorithm

In this paper, a new nonparametric Thevenin equivalent parameters (TEPs) estimation method is proposed by using local measurements. It is achieved by considering the TEPs estimation issue as a classical optimized denoising problem. The noise characteristics are analyzed through stochastic simulation...

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Bibliographic Details
Published in:2017 IEEE Power & Energy Society General Meeting pp. 1 - 5
Main Authors: Shaofei Shen, Huifang Wang, Peijun Hu, Benteng He, Yilu Liu, Chun Gan
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2017
Subjects:
Estimation
Impedance
Load modeling
Mathematical model
Noise reduction
nonparametric regression
optimized estimation
Stochastic processes
taut-string-multiresolution
Thevenin equivalent parameters
ISSN:1944-9933
Online Access:Get full text
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Summary:In this paper, a new nonparametric Thevenin equivalent parameters (TEPs) estimation method is proposed by using local measurements. It is achieved by considering the TEPs estimation issue as a classical optimized denoising problem. The noise characteristics are analyzed through stochastic simulation experiments. Then, a nonparametric regression algorithm named taut-string-multiresolution is employed to obtain the optimized TEPs estimation results. Compared with the classic Total Variation (TV) based denoising model, the proposed estimation method has relatively stable performance in different operation conditions, without the need for predetermination of any parameters. The effectiveness of the proposed TEPs estimation method is verified by an ideal two-bus equivalent system under two different operation scenarios.
ISSN:1944-9933
DOI:10.1109/PESGM.2017.8274376