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文章摘要
扩展Prony算法在电力系统非整次谐波检测中的应用研究
Study on the application of extended Prony algorithm in power systemnon-integral harmonics measurements
Received:November 12, 2014  Revised:January 12, 2015
DOI:
中文关键词: 扩展Prony算法  小波去噪  非整次谐波检测  FFT  信号带宽
英文关键词: extended  Prony algorithm, wavelet-denoising, non-integral  harmonics detection , FFT, signal  bandwidth
基金项目:山西省回国留学人员科研资助项目(2010-34);国网山西省电力公司科技项目资助(晋电发展[2014]88号)
Author NameAffiliationE-mail
WANG Yu* College of Electrical and Power Engineering,Taiyuan University of Technology wy844942395@163.com 
ZHAO Qingsheng College of Electrical and Power Engineering,Taiyuan University of Technology  
GUO Hehong Jinzhong Electric Power Company  
WANG Zhenqi Jinzhong Electric Power Company  
ZHANG Xuejun Shanxi University  
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中文摘要:
      准确有效地检测出电力系统中各次谐波及间谐波,对于提高电能质量是至关重要的。传统的快速傅立叶变换由于频谱泄漏等原因,无法准确检测出系统中的非整次谐波。为解决这一问题,本文提出应用扩展Prony算法检测系统的非整次谐波。首先利用小波去除噪声,很好的克服Prony算法对噪声敏感这一弱点;然后通过线性预测得到模型的特征矩阵,运用奇异值分解法求解特征矩阵得到模型的特征多项式;最后运用最小二乘法求出特征多项式的根,得到非整次谐波各参数。针对信号频带宽窄可能影响算法精度这一问题,本文对不同带宽信号进行扩展Prony分析,验证算法精度。实验分析说明了该算法的可行性和有效性。
英文摘要:
      It is especially important for improving power quality to identify all kinds of harmonics and inter-harmonics accurately and effectively. Due to the spectrum leakage, the traditional Fast Fourier Transformation algorithm (FFT) cannot accurately detect the non-integral harmonics. To address this issue, extended Prony algorithm is applied in this paper to identify the non-integral harmonics. Wavelet-denoising method is firstly used to overcome the weakness that the Prony algorithm is noise-sensitive, then model’s eigenmatrix is obtained by linear prediction and singular value decomposition is used to solve the eigenmatrix to get model’s characteristic polynomial, finally least square method is used to find the root of the characteristic polynomial to get parameters of the non-integral harmonics. According to the problem that the signal band width may affect the accuracy of the algorithm, extended Prony algorithm is used to analyze different bandwidth signal to prove the algorithm’s accuracy. Experiment analysis verifies the viability and effectiveness of the algorithm.
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