Dhandapani Ragavesh, Mitiche Imene, McMeekin Scott, Mallela Venkateswara Sarma, Morison Gordon
Department of Electrical and Communication Engineering, College of Engineering, National University of Science & Technology, Seeb P.O. Box 2322, Oman.
Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.
Entropy (Basel). 2021 Nov 25;23(12):1567. doi: 10.3390/e23121567.
This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length . Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter λ for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.
本文提出了一种用于局部放电（PD）信号去噪的新方法，该方法使用一种混合算法，将自适应分解技术与熵测度和组稀疏全变差（GSTV）相结合。首先，应用经验模态分解（EMD）技术将有噪声的传感器数据分解为固有模态函数（IMF），对IMF之间进行互信息（MI）分析以设置模态长度。然后，变分模态分解（VMD）技术将有噪声的传感器数据分解为多个带限固有模态函数（BLIMF）。通过计算BLIMF之间的MI，将BLIMF分为噪声、噪声主导和信号主导的BLIMF。最终，将噪声BLIMF从进一步处理中丢弃，使用GSTV对噪声主导的BLIMF进行去噪，并将信号BLIMF相加以重建输出信号。基于噪声主导的BLIMF的离散熵值自动选择GSTV的正则化参数λ。从信噪比、均方根误差和相关系数等性能指标方面评估了所提出的去噪方法的有效性，并将这些指标与EMD变体进行了比较，结果表明所提出的方法能够有效地对合成的方块、凸块、多普勒、重正弦、PD脉冲和实际PD信号进行去噪。
