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A Method to Evaluate Harmonic Model-Based Estimations under Non-White Measured Noise

Cuong Duc Le (Institutionen för signaler och system, Signalbehandling) ; Math Bollen ; Irene Y.H. Gu (Institutionen för signaler och system, Signalbehandling)
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011; Trondheim; 19 June 2011 through 23 June 2011 p. 4. (2011)
[Konferensbidrag, refereegranskat]

Automatic extracting information from power-system event recordings requires applications of signal-processing estimation techniques whose performance has been verified under white noise. This paper proposes a method to test these techniques under real power-system noise, which is very different from white noise, to evaluate their application feasibility. The first part of the paper describes the evaluation method used to evaluate the techniques in a statistical sense and a method to extract noise from measured power-system recordings. The second part of the paper focuses on the evaluation of a number of harmonic model-based techniques under non-white noise, including: Kalman filter, MUSIC, ESPRIT, and segmentation algorithms. The paper shows that for the Kalman filter, a very high order with high computational burden is necessary only if high frequency components are of interest. The application of MUSIC, ESPRIT, and the segmentation algorithms under natural power-system noise is shown to be feasible.

Nyckelord: Harmonic analysis, performance evaluation, power quality, signal-processing applications



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Denna post skapades 2011-06-17. Senast ändrad 2013-09-09.
CPL Pubid: 141936

 

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