%0 Conference Paper
%B 2011 North American Power Symposium (NAPS 2011)
%D 2011
%T Evaluation of mode estimation accuracy for small-signal stability analysis
%A Jim Follum
%A Ning Zhou
%A John W. Pierre
%K AA09-002
%K AARD
%K Automatic Switchable Network (ASN)
%K load modeling
%K power system monitoring
%X This paper proposes a method for determining electromechanical mode estimate accuracy by relating mode estimate error to residual values. Mode frequency and damping ratio were estimated using Prony analysis and residuals were calculated for a 17-machine model with varying levels of load noise. Mode estimate error and residuals were found to increase proportionally to each other as noise values were increased, revealing a distinctly linear relationship. The use of these results to develop appropriate confidence in models is discussed. With the relationship established, a method of predicting mode estimate error values based on residuals in the western North American power system (wNAPS) was developed. The potential of this method to evaluate the confidence level of mode estimates is examined.
%B 2011 North American Power Symposium (NAPS 2011)
%I IEEE
%C Boston, MA, USA
%P 1 - 7
%8 08/2011
%@ 978-1-4577-0417-8
%R 10.1109/NAPS.2011.6024893
%0 Conference Paper
%B 2009 IEEE Power & Energy Society General Meeting
%D 2009
%T Electromechanical mode shape estimation based on transfer function identification using PMU measurements
%A Ning Zhou
%A Zhenyu Huang
%A Luke Dosiek
%A Dan Trudnowski
%A John W. Pierre
%K AA07-001
%K AARD
%K Automatic Switchable Network (ASN)
%K phasor measurement units (PMUs)
%K Power system modeling
%X 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.
%B 2009 IEEE Power & Energy Society General Meeting
%I IEEE
%C Calgary, Canada
%P 1 - 7
%8 07/2009
%@ 978-1-4244-4241-6
%R 10.1109/PES.2009.5275924