1. 福建工程学院电子电气与物理学院
2. 武汉大学电气与自动化学院
3. ABB(中国)有限公司中压产品技术中心
4. 中能电气股份有限公司
网络出版:2021-12-30,
纸质出版:2021
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杨秋玉, 阮江军, 庄志坚, 等. 高压断路器机械振动信号的参数自适应降噪方法[J]. 高电压技术, 2021,47(12):4274-4287.
YANG Qiuyu, RUAN Jiangjun, ZHUANG Zhijian, et al. Parameter Adaptive Noise Reduction Method for Mechanical Vibration Signals of High-voltage Circuit Breakers[J]. 2021, 47(12): 4274-4287.
高压断路器动作过程中产生的机械振动信号蕴含着丰富的断路器状态信息
然而
由于受到噪声的影响
有效的状态信息往往难于提取。针对该问题
提出一种谱形态变分模态分解(SSVMD)联合快速奇异值分解(FSVD)的降噪方法。首先
根据振动信号变分模态分解(VMD)结果的频谱特征确定最优分解层数
实现信号各频率成分的自适应分解;然后
通过信号功率谱特性确定噪声频带;最后
根据噪声频带的主频成分进行FSVD与重构
实现振动信号的自适应降噪。该文提出的方法避免了传统VMD分解层数以及奇异值分解(SVD)降噪有效秩选择的盲目性
能够根据振动信号自身特点进行自适应降噪。通过仿真信号、两种不同型号断路器振动信号
从定性和定量两方面对所提降噪方法的有效性和实用性进行验证。结果表明
所提方法降噪效果较好
降噪性能优于对比方法(中值滤波、五点三次平滑法、小波降噪及经验模态分解(EMD)降噪)
为断路器振动信号降噪提供了一种较合适的方法。
Mechanical vibration signals generated during high-voltage(HV) circuit breakers(CBs) operation contain abundant CB state information. However
due to the influence of noise
effective state information is often difficult to extract. To address this problem
a novel noise reduction method based on spectral shape variational mode decomposition(SSVMD) and fast singular value decomposition(FSVD) is proposed. Firstly
the optimal number of decomposition layer of the vibration signal is determined according to the spectrum characteristics of the variational mode decomposition(VMD) results
which realizes the adaptive decomposition of the vibration signal. Then
the power spectrum of the vibration signal is calculated to determine the noise frequency band. Finally
FSVD and signal reconstruction are carried out according to the main frequency components of the noise band for realizing the adaptive noise reduction. The algorithm proposed in this paper avoids the blindness of the choice of VMD layer number and singular value decomposition(SVD)effective reconstruction rank
and can perform adaptive noise reduction according to the characteristics of vibration signal.The effectiveness and practicability of the proposed noise reduction method are verified from qualitative and quantitative aspects through simulation signal and vibration signals of two different types of CBs. The results show that the proposed method has better noise reduction performance than the denoising methods such as median filtering
five-point cubic smoothing
wavelet denoising and empirical mode decomposition(EMD)
which provides a more suitable approach for noise reduction of CB vibration signal.
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