基于交叉小波变换和主元分析的电力电子电路故障特征提取
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摘要
针对现有电力电子电路故障特征提取特征量精确度不足、分类差异性不明显以及故障提取过程易受到噪声的影响等缺点,提出一种基于交叉小波变换和主元分析的电力电子电路故障特征提取方法。该方法首先采用交叉小波变换分析故障信号,然后得出表征交叉小波谱图特性的特征量矩阵,最后利用主元分析方法降低特征量矩阵维数,剔除特征向量中的冗余信息。通过BP神经网络进行的故障诊断仿真测试,其诊断准确率达98.2%,证明了该方法的准确性。
Aiming at drawbacks of current methods for power electronics circuits feature extraction. there is not enough accuracy and not obvious classification,and the process of feature extraction is easily affected by noise. Firstly,the faulty signal of circuit information feature is analyzed and extracted by cross-wavelet transform. Then,the initial feature matrix is obtained representing cross-wavelet spectrum. Finally,principle component analysis is applied for reducing the dimension of initial feature matrix and those which redundant information are eliminated. The back propagation( BP) neural network classifiers are utilized for fault diagnosis simulation test. The results show that the fault detection accuracy is up to 98. 2%. Simulation results demonstrate the accuracy of the proposed method.
Aiming at drawbacks of current methods for power electronics circuits feature extraction. there is not enough accuracy and not obvious classification,and the process of feature extraction is easily affected by noise. Firstly,the faulty signal of circuit information feature is analyzed and extracted by cross-wavelet transform. Then,the initial feature matrix is obtained representing cross-wavelet spectrum. Finally,principle component analysis is applied for reducing the dimension of initial feature matrix and those which redundant information are eliminated. The back propagation( BP) neural network classifiers are utilized for fault diagnosis simulation test. The results show that the fault detection accuracy is up to 98. 2%. Simulation results demonstrate the accuracy of the proposed method.
引文
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[11]龙杰,张彼德,张强,等.基于PCA和MPGA优化神经网络的整流器故障诊断[J].电测与仪表,2015,52(21):86-90.Long Jie,Zhang Bide,Zhang Qiang,et al.Three-phase rectifiers fault diagnosis based on PCA and MPGA optimized neural network[J].Electrical Measurement&Instrumentation,2015,52(21):86-90.
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[14]王天真,刘远,汤天浩,等.基于相对主元分析的动态数据窗口故障检测方法[J].电工技术学报,2013,28(1):142-148.Wang Tianzhen,Liu Yuan,Tang Tianhao,et al.Dynamic Data Window Fault Detection Method Based on Relative Principal Component Analysis[J].Transactions Of China Electrotechnical Society,2013,28(1):142-148.
[2]Khomfoi S,Tolbert L M.Fault Diagnostic System for a Multilevel Inverter Using a Neural Network[J].IEEE Transactions on Power Electronics,2007,22(3):1062-1069.
[3]刘晓明,赵洋,曹云东,等.基于小波变换的交流系统串联电弧故障诊断[J].电工技术学报,2014,29(1):10-17.Liu Xiaoming,Zhao Yang,Cao Yundong,et al.Series arc fault diagnosis based on wavelet transform in ac system[J].Transactions of China Electrotechnical Society,2014,29(1):10-17.
[4]陈靖秋,向强铭,刘智俊,等.基于改进小波分析的电网故障诊断研究[J].电测与仪表,2015,(10):119-123.Chen Jingqiu,Xiang Qiangming,Liu Zhijun,et al.Research On Power Fault Diagnosis Based On Improved Wavelet Theory[J].Electrical Measurement&Instrumentation,2015,(10):119-123.
[5]李晓斌,赵东阳,钟光强,等.基于小波能量谱的输电线路雷击故障识别方法研究[J].电测与仪表,2014,51(12):34-39.Li Xiaobin,Zhao Dongyang,Zhong Guangqiang,et al.Identification Method Of Lightning Fault For Transmission Lines Based On Wavelet Energy Spectrum[J].Electrical Measurement&Instrumentation,2014,51(12):34-39.
[6]罗志增,李亚飞,孟明,等.脑电信号的混沌分析和小波包变换特征提取算法[J].仪器仪表学报,2011,(1):33-39.Luo Zhizeng,Li Yafei,Meng Ming,et al.EEG feature extraction algorithm based on chaos analysis and wavelet packet transform[J].Chinese Journal of Scientific Instrument,2011,(1):33-39.
[7]Garg A,Xu D,Blaber A P.Statistical validation of wavelet transform coherence method to assess the transfer of calf muscle activation to blood pressure during quiet standing[J].Biomedical Engineering Online,2013,12(1):53-63.
[8]Banerjee S,Mitra M.Application of Cross Wavelet Transform for ECG Pattern Analysis and Classification[J].IEEE Transactions on Instrumentation&Measurement,2014,63(63):326-333.
[9]尚海昆,苑津莎,王瑜,等.基于交叉小波变换和相关系数矩阵的局部放电特征提取[J].电工技术学报,2014,29(4):274-281.Shang Haikun,Yuan Jinsha,Wang Yu,et al.Feature Extraction for Partial Discharge Based on Cross-Wavelet Transform and Correlation Coefficient Matrix[J].Transactions Of China Electrotechnical Society,2014,29(4):274-281.
[10]张作良,胡志坤,何多昌,等.小波消噪在电力电子装置故障检测中的应用[J].变流技术与电力牵引,2007,(1):48-51.Zhang Zuoliang,Hu Zhikun,He Duochang,et al.Application of Wavelet De-noising in Detection of Power Electronics Faults[J].Converter Technology&Electric Traction,2007,(1):48-51.
[11]龙杰,张彼德,张强,等.基于PCA和MPGA优化神经网络的整流器故障诊断[J].电测与仪表,2015,52(21):86-90.Long Jie,Zhang Bide,Zhang Qiang,et al.Three-phase rectifiers fault diagnosis based on PCA and MPGA optimized neural network[J].Electrical Measurement&Instrumentation,2015,52(21):86-90.
[12]Rajamani P,Dey D,Chakravorti S.Classification of dynamic insulation failures in transformer winding during impulse test using crosswavelet transform aided foraging algorithm[J].Iet Electric Power Applications,2010,4(9):715-726.
[13]Dey D,Chatterjee B,Chakravorti S,et al.Rough-granular Approach for Impulse Fault Classification of Transformers using Cross-wavelet Transform[J].IEEE Transactions on Dielectrics&Electrical Insulation,2008,15(5):1297-1304.
[14]王天真,刘远,汤天浩,等.基于相对主元分析的动态数据窗口故障检测方法[J].电工技术学报,2013,28(1):142-148.Wang Tianzhen,Liu Yuan,Tang Tianhao,et al.Dynamic Data Window Fault Detection Method Based on Relative Principal Component Analysis[J].Transactions Of China Electrotechnical Society,2013,28(1):142-148.