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Light Flicker Evaluation Of Electric Arc Furnaces Based On Novel Signal Processing Algorithms

In this research work, two new flickermeters are proposed to estimate the light flicker caused by electric arc furnaces (EAFs) where the system frequency deviates significantly. In these methods, analytical expressions of the instantaneous
light flicker sensation are obtained beginning from a voltage waveform and these expressions are used to obtain a flicker estimation method based on the IEC (International Electrotechnical Commission) flickermeter. First method is a spectral decomposition based approach using DFT to estimate the light flicker. The leakage effect of the DFT algorithm due to fundamental frequency variation is reduced by employing spectral amplitude correction procedure around the fundamental frequency. Second method is a Kalman filter based approach, in which the frequency domain components of the voltage waveform are obtained by Kalman filtering. Then these components are used to obtain the light flicker.
Since the frequency decomposition is obtained by Kalman filtering, no leakage effect of the DFT is involved in case of frequency deviations which is an important advantage. Both methods are tested on both simulated data and field data
obtained from three different EAF plants where the flicker level and frequency variation is considerably high. The comparison with the digital realization of the IEC flickermeter shows that the methods are successful in estimating light
flicker with low computational complexity. The methods are especially useful for conditions such as disturbances and subsequent system transients where the system frequency deviates significantly, since the methods avoid the need for
online sampling rate adjustment to prevent the DFT leakage effect.

Identiferoai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12611074/index.pdf
Date01 September 2009
CreatorsKose, Neslihan
ContributorsLeblebicioglu, Kemal
PublisherMETU
Source SetsMiddle East Technical Univ.
LanguageEnglish
Detected LanguageEnglish
TypeM.S. Thesis
Formattext/pdf
RightsTo liberate the content for public access

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