Optimal design and low noise realization of digital differentiator

Goswami, Om Prakash; Shukla, Aasheesh; Kumar, Manish

doi:10.2478/jee-2022-0044

Cited by 2 publications

(1 citation statement)

References 17 publications

(21 reference statements)

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“…Goswami manifested the minimization of the ℓ 1error objective function by using a hybrid optimization technique consisting of the particle swarm and simulated annealing optimization algorithm. But it is only used for design of a differentiator in the digital domain [28]. However, in the application of GMC to ECG de-noising, the SNR and root mean square error (RMSE) based on -norm have been significantly improved [29].…”

Section: Introductionmentioning

confidence: 99%

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Chen

Zhang

et al. 2022

Journal of Electrical Engineering

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In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.

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Section: Introductionmentioning

confidence: 99%