Name
Abstract | ||
|---|---|---|
The elimination of a variety of noises such as the narrow-band interference in the detection of partial discharge (PD) signals in switchgear is an intractable issue. Furthermore, the self-adaptation in the denoising process is weak. A partial discharge-based novel adaptive ensemble empirical mode decomposition (Novel Adaptive EEMD, NAEEMD) method is proposed in this paper for noise reduction. First, the signal is decomposed using the EEMD, only the first-order natural mode is decomposed until the signal margin reaches the EEMD decomposed termination condition. After removing the first-order mode, noise is added to the residual signal, and the remaining signal components are decomposed in the next stage. At last, the intrinsic mode function (IMF) of the noise reduction reconstruction is adaptively selected. The latter is accomplished by combining the energy density and the average period of the IMF correlation coefficient method. Meanwhile, the proposed method provides a new strategy for pre-processing the PD signal of the switchgear. The outcomes of the proposed NAEEMD de-noising method have been compared with the conventional wavelet denoising algorithm (WDA) and EMD-based threshold denoising for validation. The simulation results showed a good denoising effect and effectiveness of the proposed method compared to the WDA and EMD-based threshold denoising. Furthermore, an experimental simulation utilizing actual switchgear PD signal has been performed to verify the noise reduction effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2914064 | IEEE ACCESS |
Keywords | Field | DocType |
Switchgear,partial discharge,NAEEMD,narrow-band interference,denoising,wavelet transform | Noise reduction,Partial discharge,Computer science,Electronic engineering,Switchgear,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 1 |
PageRank | References | Authors |
0.37 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Jin | 1 | 1 | 1.04 |
| Qiangguang Li | 2 | 1 | 0.37 |
| Mohamed A. Mohamed | 3 | 1 | 0.70 |