含窄带噪声和白噪声的复杂染噪局部放电信号提取及应用

复杂噪声环境下，电力设备局部放电信号的高完备度提取是实现其运行状态在线评估的关键。该文提出一种基于自适应噪声的总体集合经验分解(CEEMDAN)和改进小波包结合的复杂染噪局放信号提取方法。首先，通过自适应CEEMDAN将染噪信号进行分解，利用奇异值分解(SVD)算法对分量中包含的窄带噪声和频率混叠进行抑制，再根据信号间的相关系数确定有效分量并重构。最后，采用改进的小波包阈值法对重构信号中的白噪声进行滤除。利用该文算法分别对仿真数据和实测数据进行去噪处理，定量分析表明，该方法可有效去除白噪声和窄带噪声干扰，提取的局放信号波形畸变小、能量损失小，能够满足后续的工程应用需求。

Denoising of Complex Noisy Partial Discharge Pulses with Narrowband Interference and White Noise

In a complex noise environment, the high completeness extraction of partial discharge (PD) signals of power equipment is the key to the online evaluation of its status. This paper proposes a method for extracting complex noisy PD signals based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) and wavelet packet. Firstly, the noisy PD signal is decomposed by adaptive CEEMDAN, and the narrowband noise and frequency aliasing contained in the component are suppressed by using singular value decomposition (SVD) algorithm. Then, the effective components are determined according to the correlation coefficient to reconstruct the signals. Finally, the modified wavelet packet threshold method is employed to filter the white noise in the reconstructed signal. The algorithm is used to denoise the simulated data and the measured data separately. The quantitative analysis results show that the method can effectively remove white noise and narrowband noise interference. The waveform of the extracted PD signal has small distortion and energy loss, which can meet the subsequent application.