NDLI: Electromechanical mode shape estimation based on transfer function identification using PMU measurements

Content Provider	IEEE Xplore Digital Library
Author	Zhou, N. Huang, Z. Dosiek, L. Trudnowski, D. Pierre, J.W.
Copyright Year	2009
Description	Author affiliation: Montana Tech of The University of Montana, Butte, MT 59701, USA (Trudnowski, D.) \|\| Pacific Northwest National Lab, Richland, WA 99352, USA (Zhou, N.) \|\| University of Wyoming, Laramie, WY 82071, USA (Dosiek, L.; Pierre, J.W.) \|\| Zhenyu Huang is with Pacific Northwest National Lab, Richland, WA 99352, USA (Huang, Z.)
Abstract	Power system mode shapes are a key indication of how dynamic components participate in low-frequency oscillations. Traditionally, mode shapes are calculated from a linearized dynamic model. For large-scale power systems, obtaining accurate dynamic models is very difficult. Therefore, measurement-based mode shape estimation methods have certain advantages, especially for the application of real-time small signal stability monitoring. In this paper, a measurement-based mode shape identification method is proposed. The general relationship between transfer function (TF) and mode shape is derived. As an example, a least square (LS) method is implemented to estimate mode shape using an autoregressive exogenous (ARX) model. The performance of the proposed method is evaluated by Monte-Carlo studies using simulation data from a 17-machine model. The results indicate the validity of the proposed method in estimating mode shapes with reasonably good accuracy.
Starting Page	1
Ending Page	7
File Size	1766082
Page Count	7
File Format	PDF
ISBN	9781424442416
ISSN	19449925
DOI	10.1109/PES.2009.5275924
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-26
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Shape measurement Transfer functions Phasor measurement units Power system dynamics Power system modeling Power system measurements Power system stability Large-scale systems Monitoring Least squares approximation system identification Electromechanical dynamics mode shape small signal stability synchronized phasor measurements
Content Type	Text
Resource Type	Article

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