DocumentCode :
1632903
Title :
Adaptive tracking of ambient system oscillations by nonstationary RLS techniques
Author :
Moreno, I. ; Messina, A.R.
Author_Institution :
Dept. of Electr. Eng., Cinvestav, Guadalajara, Mexico
fYear :
2011
Firstpage :
1
Lastpage :
8
Abstract :
Measured ambient data in power system are known to exhibit noisy, nonstationarity fluctuations resulting primarily from small magnitude, random changes in load. Accounting for stochastic and time-varying features can provide a better description of the data and result in improved estimation algorithms. In this paper, a new hybrid algorithm combining a recursive least-square (RLS) algorithm and a Kalman filter described by a random walk correlation model is proposed to characterize the time evolution of ambient system oscillations. Extensions and generalizations to current RLS algorithms to deal with nonstationarity are discussed and the relationship between Kalman filter parameters and RLS algorithms is analyzed. Examples of the developed procedures to track the evolving dynamics of critical system modes in both simulated and measured data are presented. Comparisons with well-established approaches such as the exponentially-weighted RLS algorithm, RLS algorithms with adaptive memory, least-mean squares (LMS) algorithms and normalized LMS algorithms demonstrate the accuracy of the proposed procedure.
Keywords :
adaptive Kalman filters; correlation methods; least mean squares methods; oscillations; power system measurement; recursive estimation; stochastic processes; Kalman filter; adaptive memory; adaptive tracking; ambient system oscillation; correlation model; exponentially-weighted RLS algorithm; measured data; nonstationarity fluctuation; nonstationary RLS technique; normalized LMS algorithm; recursive least mean square algorithm; simulated data; stochastic feature; time-varying feature; Adaptive filters; Correlation; Filtering theory; Kalman filters; Mathematical model; Noise; Power systems; Ambient power system data; Kalman filtering; LMS algorithm; RLS algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Power and Energy Society General Meeting, 2011 IEEE
Conference_Location :
San Diego, CA
ISSN :
1944-9925
Print_ISBN :
978-1-4577-1000-1
Electronic_ISBN :
1944-9925
Type :
conf
DOI :
10.1109/PES.2011.6039658
Filename :
6039658
Link To Document :
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