Shang Haikun, Li Yucai, Xu Junyan, Qi Bing, Yin Jinliang
Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.
School of Electrical and Electronic Engineering, Tianjin University of Technology, Tianjin 300384, China.
Entropy (Basel). 2020 Sep 17;22(9):1039. doi: 10.3390/e22091039.
To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.
为消除局部放电（PD）检测中白噪声的影响，我们提出了一种基于带自适应噪声的完全集合经验模态分解（CEEMDAN）和近似熵（ApEn）的新方法。通过在分解过程中引入自适应噪声，CEEMDAN能够有效地将原始信号分离为具有不同频率尺度的不同固有模态函数（IMF）。随后，计算每个IMF的近似熵值以消除有噪声的IMF。然后，采用相关系数分析来选择代表主要局部放电特征的有用IMF。最后，提取真实的IMF用于局部放电信号重构。基于EEMD，CEEMDAN可以进一步提高重构精度并减少迭代次数以解决模态混叠问题。对模拟和现场局部放电信号的结果表明，所提出的方法可有效地用于噪声抑制并成功提取局部放电脉冲。该融合算法结合了CEEMDAN算法和ApEn算法各自的优点，并且比EMD和EEMD具有更好的去噪效果。
