A Dynamic Harmonic Regression Approach to Power System Modal Identification and Prediction

Abstract

Abstract—Time series models provide a powerful tool to extract nonstationary features from measured data. In this article, a statistical framework based upon a dynamic harmonic regression model for examining modal behavior is provided. In this model, temporal patterns in measured data are modeled within a stochastic state space setting. Estimates of the states or time-varying parameters are then obtained using an optimal estimation method based on the Kalman filter. Techniques to estimate future values of the unobserved signal are also analyzed. The widely applicable technique is illustrated on both simulated and measured data. Factors that affect the performance of the method are discussed, including the effects of non-linear trends, data quality, and sampling design. Connections with other modal identification methods are also investigated.

Keywords:

• dynamic harmonic regression
• Kalman filter
• Prony analysis
• trend identification
• unobserved component model

Additional information

Funding

The authors gratefully acknowledge financial support from CONACYT, Mexico, under grant 234455.

Notes on contributors

Armando Jiménez Zavala

Armando Jiménez Zavala received his B.Sc. degree in electrical engineering from Morelia Institute of Technology, Michoacan, Mexico, in 2007, and the M.Sc. degree in electrical engineering from Cinvestav, Mexico, in 2012. He is currently working toward a doctoral degree in electrical engineering at Cinvestav. He is a Student Member of the IEEE. His research interests include measurement-based methods to the study of power system oscillations, and power system identification.

Arturo R. Messina

Arturo R. Messina received the M.Sc. degree (Honors) in electrical engineering from the National Polytechnic Institute of Mexico, in 1987, and the Ph.D. degree from Imperial College of Science Technology and Medicine, London, U.K., in 1991. Since 1997, he is a professor at the Center for Research and Advanced Studies (Cinvestav) of the National Polytechnic Institute of Mexico. He is an Editor of the Electric Power Components and Systems Journal. His areas of interest include power system stability analysis and control, and the development and application of advanced measurement-based signal processing techniques to the study and characterization of inter-area oscillations in power systems.