Abstract
While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.
Highlights
The time-varying kurtosis method was demonstrated to accurately determine the partial discharge (PD) signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB
The electrical insulation of high-voltage power cables is subject to stresses that can result in local dielectric breakdowns that are typically preceded by partial discharge (PD) pulse signals
Localization algorithms employing distance measurements are based on various PD signal features, such as time difference of arrival (TDOA) [5], time of arrival (TOA) [6], angle of arrival (AOA) [7], or received signal strength indicator (RSSI) [8]
Summary
The electrical insulation of high-voltage power cables is subject to stresses that can result in local dielectric breakdowns that are typically preceded by partial discharge (PD) pulse signals. The fault localization accuracies of algorithms based on the arrival time difference threshold method [9] and the half-wave-based cross-correlation method [10] are limited by the SNR of the PD signal. The EMD-based denoising method realizes the adaptive decomposition of signals with great flexibility This method suffers from the frequency aliasing problem and the endpoint effects remaining after conducting the decomposition cannot be suppressed, which is problematic when attempting to select signal components that make a significant contribution to the PD signal, because significant components that are not readily detectable can be omitted, and negatively impact subsequent signal analysis. This paper addresses the above-discussed challenges by proposing a signal extraction algorithm based on complete ensemble EMD with adaptive noise (CEEMDAN) to detect significant signal components and suppress periodic narrowband noise.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
