Dealing with noise polluting electric signals acquired from a large power group

Nicolae, Ileana-Diana; Nicolae, Petre-Marian; Marinescu, Radu-Florin

doi:10.1109/isie.2017.8001227

Cited by 5 publications

(1 citation statement)

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“…A correct denoising allows for a more accurate evaluation of power quality indices and detection of faults by using wavelet based decompositions [3]. When the analyzed data have a quasi-stationary nature and are polluted by white noise, a special technique (relying on average signals) can be applied [4]... [7]. According to it, repetitive additions of noisy signals tend to emphasize their systematic characteristics and to cancel out any zero-mean random noise.…”

Section: Introductionmentioning

confidence: 99%

Comparative study of wavelet based techniques for electromagnetic noise evaluation and removal

Nicolae

Popescu

et al. 2020

ITM Web Conf.

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Signals acquired from an industrial environment with many sources of electromagnetic interferences may be polluted by white noise. Polluted data segments with many steady consecutive periods can be used (sometimes unsuccessful) for the estimation of a denoised period from the steady acquired data by using the mean signal method. For data segments with at least 4 periods, when only certain segments (shorter than a period) can be considered steady, hybrid algorithms can be used to automatically evaluate the power of noise and afterward to perform the noise removal by using wavelet thrashing trees. This paper deals with 2 additional denoising techniques. The 1-st one is based on the Wavelet Package Transform and allows for the separation of the noise components which pollute a data segment of at least one period. The second approached denoising technique is also addressing one period data segments and estimates ﬁrstly the power of noise by using the energies of the vectors of details from the ﬁrst 2 levels of a tree used by decompositions with the Stationary Wavelet Transform. The estimated power of noise is afterward used to establish the number of levels in the wavelet thrashing trees. In this last stage, two wavelet mothers were used. Simulated and real test signals were used and performance comparisons were performed.

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Section: Introductionmentioning

confidence: 99%