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Signal Processing Methods for Estimating Small-Signal Dynamic Properties from Measured Responses

  • Daniel Trudnowski
  • John Pierre
Chapter
Part of the Power Electronics and Power Systems book series (PEPS)

Abstract

Power system small-signal electromechanical dynamic properties are often described using linear system concepts. The underlying hypothesis is that small motions of the system can be described by a set of ordinary differential equations. Modal analysis of these governing equations provides considerable insight into the stability properties of the system. Over the past two decades, many signal processing techniques have been developed to conduct modal analysis using only time-synchronized actual system measurements. Some techniques are appropriate for transient signals, others are for ambient signal conditions, and some are for conditions where a known probing signal is exciting the system. In this chapter, an overview of many of the more successful analysis techniques is presented. The theoretical basis for these methods is described as well as application properties and performance. Examples include computer simulations and actual system experiments from the western North American power system. Analysis goals center on estimating the modal properties of the system including modal frequency, damping, and shape.

Keywords

Power System Power Spectral Density Mode Shape Ambient Noise Mode Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors wish to acknowledge the contribution of the many graduate students over the years. Also, the technical leadership of Dr. John Hauer of Pacific Northwest National Laboratory (retired) and Mr. Bill Mittelstadt of the Bonneville Power Administration (retired) are acknowledged. Much of this work was supported by the US Department of Energy; the authors wish to thank Mr. Phil Overholt for his support.

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Copyright information

© Springer Science+Business Media,LLC 2009

Authors and Affiliations

  1. 1.Electrical Engineering DepartmentMontana Tech of the University of MontanaButteUSA

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