Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

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.

Robust Estimation of Arrival Time of Complex Noisy Partial Discharge Pulse in Power Cables Based on Adaptive Variational Mode Decomposition

“…As shown in Figure 6c,d, the de-noising PD signal extracted by the proposed method combined adaptive VMD and adaptive wavelet packet decomposition maintains a high degree of similarity with the original signal in amplitude and phase. In order to analyze the results quantitatively, evaluation parameters such as NCC, SNR, and variational trend parameter (VTP) [22] is calculated in Table 3. VTP represents the similarity of the two waveforms' changing trends, and measures the oscillation of the waveforms to a certain extent.…”

“…where m k is the k-order statistic of random variable X. According to formula (22), kurtosis value only represents the overall steepness of the signal, and cannot reflect the real-time change of the steepness of time-varying signal. Therefore, time-varying kurtosis algorithm should be further adopted.…”

“…Therefore, time-varying kurtosis algorithm should be further adopted. In the discrete sequence X(i) to be detected, a sub-time window with length b and sampling point i as the center is designed, the kurtosis value of the signal in the sub-time window is obtained according to formula (22), and time-varying kurtosis value is defined according to the following formula:…”

“…Noise suppression based on empirical mode decomposition (EMD) [20,21] can decompose multi-time-scale noisy PD signals into single time-scale intrinsic mode functions (IMF) adaptively, it is suitable for non-stationary and nonlinear PD signals. However, the following defects limit its further usage [22]. The central frequency and bandwidth of the noise interferences associated with the PD signal in the detected noisy PD signal are uncertain.…”

A Novel Hybrid Approach for Partial Discharge Signal Detection Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Approximate Entropy

“…Wavelet transform is suitable for processing non-stationary signal with better time-frequency resolution performance [ 5 , 6 , 7 ]. It has been widely researched in PD signal de-noising and achieved excellent application effectiveness [ 8 , 9 , 10 ]. Dai et al [ 8 ] present a denoising method based on improved portogram and wavelet transform for UHF PD signals.…”