基于经验小波变换的特高压并联电抗器振动信号分析
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摘要
为有效分析特高压并联电抗器振动信号特征,提出一种基于经验小波变换的特高压并联电抗器振动信号分析方法。该方法通过对信号傅里叶谱自适应的分割,在每个区间内构造正交小波滤波器来提取具有紧支撑傅里叶谱的固有模态,对提取出的模态成分进行希尔伯特变换,得到每一个分量的瞬时频率和振幅;综合仿真信号和实测特高压并联电抗器振动信号,将经验小波变换与经验模态分解两种方法进行探索性对比分析研究。研究结果表明,经验小波变换具有完备的小波理论支撑,可以自适应地设置信号模态分解层数,改善了经验模态分解存在的端点效应、模态混叠等现象,能更准确地体现振动信号的特征,区分特高压并联电抗器的不同运行状态。该方法为特高压并联电抗器振动信号特征提取与安全运行监测提供了一种新的手段。
In order to effectively analyze the characteristics of vibration signals of UHV shunt reactors, a vibration signal analysis method for UHV shunt reactors based on empirical wavelet transform is proposed. In this method, an adaptive wavelet spectrum segmentation method is used to construct an orthogonal wavelet filter in each interval to extract an intrinsic mode with a tightly-supported Fourier spectrum, and the extracted modal components are subjected to Hilbert to get the instantaneous frequency and amplitude of each component. Combining synthetic simulation signal with measured UHV shunt reactor vibration signal, the empirical wavelet transform and empirical mode decomposition are used to make exploratory comparative analysis. The research results show that the empirical wavelet transform has a complete wavelet theory support and can adaptively set the number of signal mode decomposition layers to improve the end effect, modal aliasing and other phenomena of the empirical mode decomposition, and can more accurately reflect the vibration.The characteristics of the signal can distinguish the different operating conditions of UHV shunt reactors. This method provides a new way for the feature extraction and safe operation monitoring of the UHV shunt reactor vibration signal.
In order to effectively analyze the characteristics of vibration signals of UHV shunt reactors, a vibration signal analysis method for UHV shunt reactors based on empirical wavelet transform is proposed. In this method, an adaptive wavelet spectrum segmentation method is used to construct an orthogonal wavelet filter in each interval to extract an intrinsic mode with a tightly-supported Fourier spectrum, and the extracted modal components are subjected to Hilbert to get the instantaneous frequency and amplitude of each component. Combining synthetic simulation signal with measured UHV shunt reactor vibration signal, the empirical wavelet transform and empirical mode decomposition are used to make exploratory comparative analysis. The research results show that the empirical wavelet transform has a complete wavelet theory support and can adaptively set the number of signal mode decomposition layers to improve the end effect, modal aliasing and other phenomena of the empirical mode decomposition, and can more accurately reflect the vibration.The characteristics of the signal can distinguish the different operating conditions of UHV shunt reactors. This method provides a new way for the feature extraction and safe operation monitoring of the UHV shunt reactor vibration signal.
引文
[1] 殷桂梁,伍麒伊(Yin Guiliang,Wu Qiyi).基于UKF和IGG改进聚类算法的特高压交流输电线路参数辨识(Identification method of UHV AC transmission line parameters based on UKF and IGG improved clustering)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2017,36(6): 69-75.
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[4] 向玲,唐贵基,胡爱军(Xiang Ling, Tang Guiji, Hu Aijun). 旋转机械非平稳振动信号的时频分析比较(Vibration signals time-frequency analysis and comparison for a rotating machinery)[J]. 振动与冲击(Journal of Vibration and Shock),2010,29(2): 42-45.
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[10] 王秋生,陈璐,袁海文,等(Wang Qiusheng, Chen Lu, Yuan Haiwen, et al.). 基于经验小波变换的电晕电流降噪方法(Corana current de-noising method based on empirical wavelet transform)[J]. 电网技术(Power System Technology),2017,41(2): 670-676.
[11] Pan Jun, Chen Jinglong, Zi Yanyang, et al.Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment[J]. Mechanical System and Signal Processing,2016, 72-73:160-183.
[12] 武哲,杨绍普,刘永强(Wu Zhe, Yang Shaopu, Liu Yongqiang). 基于多元经验模态分解的旋转机械早期故障诊断方法(Early fault diagnosis method of rotating machinery based on multivariate empirical mode decomposition)[J].仪器仪表学报(Chinese Journal of Scientific Instrument),2016,37(2): 241-248.
[13] 王玉梅,史泽亚,谷斐,等(Wang Yumei,Shi Zeya,Gu Fei,et al.).基于边界延拓改进EMD的断路器机械故障诊断法(Novel diagnostic method for mechanical fault of high-voltage circuit breaker based on the improved EMD with boundary extension)[J].高压电器(High Voltage Apparatus),2017,53(12): 99-105.
[14] 冯晓丽,王智勇,杨紫明,等(Feng Xiaoli,Wang Zhiyong,Yang Ziming, et al.).基于HHT的单次电弧辐射电磁噪声时频分析(Time-frequency analysis of electromagnetic noise produced by single arc based on HHT)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2018,37(4): 57-63.
[2] 章勇高,常凯旋,苏永春(Zhang Yonggao, Chang Kaixuan, Su Yongchun). 特高压接入电网的并联电抗器补偿研究(Compensation of UHV access grid by shunt reactors)[J]. 高压电器(High Voltage Apparatus),2016,52(1): 1-6, 14.
[3] 杨意妹,祝丽花,王斌,等(Yang Yimei,Zhu Lihua,Wang Bin, et al.). 考虑磁致伸缩效应的可控饱和电抗器电磁振动分析(Analysis on electromagnetic vibration of controllable saturable reactor considering magnetostrictive effects)[J]. 电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2016,35(8): 40-45.
[4] 向玲,唐贵基,胡爱军(Xiang Ling, Tang Guiji, Hu Aijun). 旋转机械非平稳振动信号的时频分析比较(Vibration signals time-frequency analysis and comparison for a rotating machinery)[J]. 振动与冲击(Journal of Vibration and Shock),2010,29(2): 42-45.
[5] 吴杰,丁明(Wu Jie, Ding Ming).采用自适应小波包分解的混合储能平抑风电波动控制策略(Wind power fluctuation smoothing strategy of hybrid energy storage system using self-adaptive wavelet packet decomposition)[J].电力系统自动化(Automation of Electric Power Systems),2017,41(3): 7-12.
[6] Huang N E,Shen Z,Long R,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society London A - Mathematical, Physical and Engineering Sciences,1998, 454: 903-995.
[7] 苏玉香,刘志刚,李科亮,等(Su Yuxiang,Liu Zhigang,Li Keliang, et al.).一种改善EMD端点效应的新方法及其在谐波分析中的应用(A new method for end effect of EMD and its application to harmonic analysis)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2008, 27(2): 33-37.
[8] Gilles J. Empirical wavelet transform [J]. IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[9] Gilles J,Tran G,Osher S. 2D empirical transforms: Wavelets,ridgelets,and curvelets revisited[J]. SIAM Journal on Imaging Sciences,2014, 7(1):157-186.
[10] 王秋生,陈璐,袁海文,等(Wang Qiusheng, Chen Lu, Yuan Haiwen, et al.). 基于经验小波变换的电晕电流降噪方法(Corana current de-noising method based on empirical wavelet transform)[J]. 电网技术(Power System Technology),2017,41(2): 670-676.
[11] Pan Jun, Chen Jinglong, Zi Yanyang, et al.Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment[J]. Mechanical System and Signal Processing,2016, 72-73:160-183.
[12] 武哲,杨绍普,刘永强(Wu Zhe, Yang Shaopu, Liu Yongqiang). 基于多元经验模态分解的旋转机械早期故障诊断方法(Early fault diagnosis method of rotating machinery based on multivariate empirical mode decomposition)[J].仪器仪表学报(Chinese Journal of Scientific Instrument),2016,37(2): 241-248.
[13] 王玉梅,史泽亚,谷斐,等(Wang Yumei,Shi Zeya,Gu Fei,et al.).基于边界延拓改进EMD的断路器机械故障诊断法(Novel diagnostic method for mechanical fault of high-voltage circuit breaker based on the improved EMD with boundary extension)[J].高压电器(High Voltage Apparatus),2017,53(12): 99-105.
[14] 冯晓丽,王智勇,杨紫明,等(Feng Xiaoli,Wang Zhiyong,Yang Ziming, et al.).基于HHT的单次电弧辐射电磁噪声时频分析(Time-frequency analysis of electromagnetic noise produced by single arc based on HHT)[J].电工电能新技术(Advanced Technology of Electrical Engineering and Energy),2018,37(4): 57-63.