Online partial discharge (PD) monitoring is an im-portant means to detect insulation deterioration. However, it is dif-ficult to extract PD signal due to various interference in the field. Noisy PD signal is used to judge the status of insulation, which would affect the conclusion, therefore de-noising PD signal is a ma-jor task in online PD monitoring. Common methods for PD de-noising include the empirical mode decomposition (EMD) and wavelet transform; however, the de-noising results are highly de-pendent on the modal aliasing, the selection of mother wavelets and decomposition levels. This paper proposes a method to solve these problems. This method uses traditionally singular value transform (SVD) to reconstruct narrow-band interference and re-move it. Next, the empirical wavelet transform (EWT) is carried out for the PD signal which has residual white noise. Then, the noisy signal is decomposed into several modes corresponding to each spectrum segment. 3σ principle is used to de-noise 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-do-main de-noising. The results from both simulated and actual field detection signals show the excellent performance of this method.

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 3σ 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.

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.

Target detection in sea clutter is of great significance in military radar research. Because the characteristics of sea clutter are complex and easily affected by wind direction, the method of suppressing sea clutter by using sea clutter characteristics to reproduce is not advantageous. To address this problem, this paper focuses on direct extraction of target echo. According to the difference of fractional characteristics between target echo and sea clutter, fractional Fourier transform is introduced to extract target echo. In this paper, a fractional domain change coefficient clustering method is established to identify the range units of target, and a fractional domain filtering method based on the minimax is proposed to extract the target spectrum of target dominated range units, so as to separate the target echo from sea clutter. Simulated and measured experiments prove that this method can accurately identify targets and extract target information under various sea state and different number of targets.