An Ultrahigh Frequency Partial Discharge Signal De-Noising Method Based on a Generalized S-Transform and Module Time-Frequency Matrix

Abstract

Due to electromagnetic interference in power substations, the partial discharge (PD) signals detected by ultrahigh frequency (UHF) antenna sensors often contain various background noises, which may hamper high voltage apparatus fault diagnosis and localization. This paper proposes a novel de-noising method based on the generalized S-transform and module time-frequency matrix to suppress noise in UHF PD signals. The sub-matrix maximum module value method is employed to calculate the frequencies and amplitudes of periodic narrowband noise, and suppress noise through the reverse phase cancellation technique. In addition, a singular value decomposition de-noising method is employed to suppress Gaussian white noise in UHF PD signals. Effective singular values are selected by employing the fuzzy c-means clustering method to recover the PD signals. De-noising results of simulated and field detected UHF PD signals prove the feasibility of the proposed method. Compared with four conventional de-noising methods, the results show that the proposed method can suppress background noise in the UHF PD signal effectively, with higher signal-to-noise ratio and less waveform distortion.

A Novel Partial Discharge Ultra-High Frequency Signal De-Noising Method Based on a Single-Channel Blind Source Separation Algorithm

“…For the RS algorithm, the constructed Hankel matrix is 15,000 by order 5001, and the larger number of singular values is 18 [11]. For the GSMT algorithm, the adjusted parameter of the generalized S-transform is 0.3, and the number of effective singular values is 4 [13]. Reverse separation based on independent component analysis (RS) Method 5…”

“…A sparse representation de-noising method was proposed in [12], but it needs to establish the atomic library and takes a long time to complete the iteration calculation. A generalized S-transform module time-frequency matrix method (GSMT) was proposed for de-noising the PD signals in [13], but it could not effectively suppress the amplitude modulated narrow-band interference during wireless communication, and large amounts of matrix operations were needed. To suppress the Gaussian white noise, a mathematical morphology filters (MMF) method was proposed to suppress the Gaussian white noise in [14], and a novel singular value decomposition (SVD) method was proposed to suppress the Gaussian white noise and the original PD signal could be more accurately recovered in [15].…”

Gated Pipelined Folding ADC-Based Low Power Sensor for Large-Scale Radiometric Partial Discharge Monitoring

“…The employment of a logarithmic responding power detector provides the additional benefit of effectively linearizing the envelope of the received signal, since the received radiometric PD signal can be modelled by the following expression [9]:…”

“…The fundamental technique involves using radio receivers to detect and measure the electromagnetic signal propagated from the 1 -1000 ns current pulse displaced during the PD event. This electromagnetic pulse signal resembles a classical decaying oscillation [9] within a bandwidth of 50 -1000 MHz, depending on the type and structure of the fault [10]- [19]. However, the frequency range is generally limited to approximately 50 -800 MHz due to the band-limiting response of the propagation environment containing various high-frequency attenuating metallic structures [20].…”

A Time–Frequency Correlation Analysis Method of Time Series Decomposition Derived from Synchrosqueezed S Transform

“…The generalized S transform [13] and the modified S transform have been proposed to deal with the TF resolution, but they have not solved the problem perfectly. Synchrosqueezed transform (SST) [14,15] has recently been identified by Daubechies et al to serve as an empirical mode decomposition-like tool.…”