Zhang Jun, He Junjia, Long Jiachuan, Yao Min, Zhou Wei
State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
School of Electronics and Information Engineering, Wuhan Donghu University, Wuhan 430212, China.
Sensors (Basel). 2019 Apr 2;19(7):1594. doi: 10.3390/s19071594.
Noise suppression is one of the key issues for the partial discharge (PD) ultra-high frequency (UHF) method to detect and diagnose the insulation defect of high voltage electrical equipment. However, most existing denoising algorithms are unable to reduce various noises simultaneously. Meanwhile, these methods pay little attention to the feature preservation. To solve this problem, a new denoising method for UHF PD signals is proposed. Firstly, an automatic selection method of mode number for the variational mode decomposition (VMD) is designed to decompose the original signal into a series of band limited intrinsic mode functions (BLIMFs). Then, a kurtosis-based judgement rule is employed to select the effective BLIMFs (eBLIMFs). Next, a singular spectrum analysis (SSA)-based thresholding technique is presented to suppress the residual white noise in each eBLIMF, and the final denoised signal is synthesized by these denoised eBLIMFs. To verify the performance of our method, UHF PD data are collected from the computer simulation, laboratory experiment and a field test, respectively. Particularly, two new evaluation indices are designed for the laboratorial and field data, which consider both the noise suppression and feature preservation. The effectiveness of the proposed approach and its superiority over some traditional methods is demonstrated through these case studies.
噪声抑制是局部放电（PD）超高频（UHF）方法检测和诊断高压电气设备绝缘缺陷的关键问题之一。然而，大多数现有的去噪算法无法同时降低各种噪声。同时，这些方法很少关注特征保留。为了解决这个问题，提出了一种新的UHF PD信号去噪方法。首先，设计了一种变分模态分解（VMD）模态数自动选择方法，将原始信号分解为一系列带宽有限的固有模态函数（BLIMF）。然后，采用基于峭度的判断规则来选择有效的BLIMF（eBLIMF）。接下来，提出了一种基于奇异谱分析（SSA）的阈值技术来抑制每个eBLIMF中的残余白噪声，并通过这些去噪后的eBLIMF合成最终的去噪信号。为了验证我们方法的性能，分别从计算机模拟、实验室实验和现场测试中采集了UHF PD数据。特别是，针对实验室和现场数据设计了两个新的评估指标，同时考虑了噪声抑制和特征保留。通过这些案例研究证明了所提方法的有效性及其相对于一些传统方法的优越性。
