Search

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance. [ABSTRACT FROM AUTHOR]

Due to complex disturbance environment, partial discharge (PD) monitor signal denoising is a necessary step to judge the state of equipment insulation. In the denoising process, it is difficult to suppress the white noise due to its wide frequency-domain distribution. To overcome these difficulties, most denoising algorithms need to artificially adjust some threshold parameters or select the number of decomposition layers according to the measured PD signals, which leads to the confusion of uncertainty, and the denoising performance depends on the parameter setting experience. Therefore, the denoising effect cannot be guaranteed in general. This article proposes a method without human intervention in choosing the threshold parameters or the decomposition layer numbers. First, the decomposition layers are directly determined from the spectral kurtogram of PD signal to obtain the reference signal. Second, the reference signal is denoised by $3\sigma $ threshold. At the start point obtained from the denoised signal and the end point obtained from the reference signal envelope, the reference signal is segmented in the time domain to obtain the final denoising PD signal. The denoising results of both the simulated and field-measured PD signals show that the proposed method can effectively remove the white noise and recover PD signal more accurately. [ABSTRACT FROM AUTHOR]

Online partial discharge (PD) monitoring is an important means to detect insulation deterioration. However, it is difficult to extract the PD signal due to various interferences in the field. Noisy PD signal is used to judge the status of insulation, which would affect the conclusion; therefore, denoising PD signal is a major task in online PD monitoring. Common methods for PD denoising include the empirical mode decomposition (EMD) and wavelet transform; however, the denoising results are highly dependent on the modal aliasing, the selection of mother wavelets, and decomposition levels. This article proposes a method to solve these problems. This method uses traditionally singular value transform [singular value decomposition (SVD)] to reconstruct narrowband interference and remove it. Next, the empirical wavelet transform (EWT) is carried out for the PD signal that has residual white noise. Then, the noisy signal is decomposed into several modes corresponding to each spectrum segment. The $3~\sigma $ principle is used to denoise the modes with large kurtosis, and the modes are combined into a reference signal. The start-end positions of PD signal are then obtained from the reference signal. Finally, the PD signal is obtained by time-domain denoising. The results from both simulated and actual field detection signals show the excellent performance of this method. [ABSTRACT FROM AUTHOR]