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.

This letter proposes a one-dimensional multiple signal classification (MUSIC) algorithm for the joint angle and range estimation in monostatic multi-input multi-output (MIMO) radar with frequency diverse array (FDA). The proposed algorithm can reduce the computational complexity greatly compared to the two-dimensional MUSIC (2D-MUSIC) algorithm but have the same performance as the 2D-MUSIC algorithm. Simulation results verify the performance of the proposed algorithm.

The existing methods estimating utility harmonic impedance usually require some assumptions such as the utility side is the dominant harmonic source and the harmonic impedance of customer side is much larger than that of the utility side, etc. However, these assumptions may not always hold in modern power grid. This paper proposes a novel method without any assumption, the estimation process only uses the correlation of the harmonic data measured at a point of common coupling (PCC). Through analysis of various field harmonic data, we find that measured data have short correlation characteristics, and there always exists a common weak correlation interval for the self-correlation and cross-correlation of the measured data. According to Norton equivalent circuit modal, it is easy to deduce that the utility harmonic current source is weak self-correlated in this interval. By selecting the self-correlation of the utility harmonic current source less than a small threshold, the utility harmonic impedance can be estimated accurately, whether the traditional assumptions are valid or not. In addition, the proposed method only requires a small sample size. Performance and effectiveness of the proposed method are verified through the simulation and field cases.

Anomaly detection in the hyperspectral image (HSI) has gradually become a hot topic in remote sensing. Recently, some tensor-based methods have been proposed to improve detection performance by exploiting the characteristic of HSI data existing in the inherent multi-dimensional. However, the existing tensor-based methods may only partially use the prior properties in both spatial and spectral dimensions. In this paper, we proposed a novel tensor ring (TR) decomposition with factors TV regularization model for hyperspectral anomaly detection. First, raw HSI data is decomposed into background and anomaly tensors. The tensor ring decomposition is adopted to exploit the low-rank property of the background existing in both spatial and spectral dimensions. Then, the total variation (TV) regularization is designed on the three dimensions of the background tensor to explore the piecewise smoothness of the background existing in both spatial and spectral dimensions. Further, this TV regularization is transferred to each factor tensor by exploiting the relationship between the background tensor and each factor tensor. Next, the l _2,1 norm regularization is designed on the anomaly tensor to exploit the group sparsity of anomaly pixels. Finally, the alternating direction method of multipliers (ADMM) scheme is adopted to update the involved variables. Experimental results validated on several real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.

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.

This article proposes a novel method for estimating multiharmonic sources in the underdetermined measurement system. In the proposed methodology, supposing that the number of harmonic measurements (HMs) is less than that of unknown state variables, the HM system can be mathematically described as underdetermined matrix equations, which has infinitely many solutions corresponding to all possibilities of the locations and the current emissions of the actual harmonic sources. Because the harmonic current emissions from nonharmonic source buses are approximately zero, by fitting the harmonic current emission of every possible nonharmonic source bus combinations and selecting the minimum fitting residual of all combination cases, thus, the true number, locations, and current emissions of harmonic sources are determined. Compared with the existing methods, the proposed method has less dependence on the placement of HMs and requires less prior information of harmonic sources. The effectiveness of the proposed method is verified by simulation and field case, with respect to a high correct location rate of more than 90%.