A Method to Evaluate Harmonic Model-Based Estimations under Non-White Measured Noise
Paper i proceeding, 2011

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.

Harmonic analysis

power quality

signal-processing applications

performance evaluation


Cuong Le

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Mathias Bollen

Luleå tekniska universitet

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011; Trondheim; 19 June 2011 through 23 June 2011

4- 6019212


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