期刊文献+

电力系统谐波分析的稳健支持向量机方法研究 被引量:60

A ROBUST SUPPORT VECTOR ALGORITHM FOR HARMONICS ANALYSIS OF ELECTRIC POWER SYSTEM
下载PDF
导出
摘要 随着电力系统谐波污染问题日益加剧,准确地掌握电网中谐波的频率分量对于电力系统的安全、经济运行具有重要的意义。文中采用基于支持向量机的稳健频谱估计算法用于电力系统谐波和间谐波的分析:采用迭代变权最小二乘法克服了常规算法中计算复杂度和时间序列的长度成指数性增长的困难:通过引入特殊的代价函数的方法消除异常值影响,使算法对异常值具有稳健性。该算法的优点是精度高,鲁棒性强,对异常值和脉冲性噪声不敏感,具有很强的稳健性。通过对受到脉冲噪声污染的直流电弧炉电流波形进行分析,同时与快速傅立叶变换和Prony方法进行对比分析,表明了该法在没有异常噪声的情况下和有大量异常噪声干扰的情况下都有相当高的分析精度,可以满足电力系统谐波和间谐波分析的要求;在matlab6.5环境下编程,计算速度也满足要求。该算法的不足之处在于为满足稳健性的要求,模型参数的选择需要模型的先验知识或由采样时间序列的统计量根据经验进行选择。 With the deterioration of harmonics pollution in power system, it is of great importance to accurately find out the harmonics component for the safe and economical operation of the power system. A novel robust approach to harmonics and interharmonics analysis, based on Support Vector Machines and solved by Iterative Reweighted Least Squares algorithm to overcome the difficulty of exponential computation complexity, is proposed in the paper. By introducing specific loss function, the method can mitigate the infection of outliers and noises and exhibits robustness characteristics. The proposed method also has high analysis precision. The application to impulse noises polluted signal of dc arc furnace installation without compensation and performance comparison with fast Fourier transform (FFT) and Prony algorithm show that the proposed method has good harmonics analysis precision to the signals both in normal and much impulsive noises contaminated conditions. Run in the Matlab version 6.5 environment, computing speed satisfies application's requirement. The shortcoming of the algorithm is that its parameters should be tuned for the best performance according to prior knowledge or the statistics of the analyzed signal.
出处 《中国电机工程学报》 EI CSCD 北大核心 2004年第12期43-47,共5页 Proceedings of the CSEE
基金 高等学校优秀青年教师教学科研奖励计划项目。
关键词 电力系统 谐波分析 间谐波 电网 谐波污染 电流波形 采样时间 支持向量机 算法 计算复杂度 Electric power engineering Power system Power quality Harmonics analysis Support Vector Machine (SVM) Robustness
  • 相关文献

参考文献14

  • 1张伏生,耿中行,葛耀中.电力系统谐波分析的高精度FFT算法[J].中国电机工程学报,1999,19(3):63-66. 被引量:482
  • 2H. Karimi, M.Karimi-Ghatermani, M.R. Iravani, et al. An Adaptive Filter for Synchronous Extraction of Harmonics and Distortions[J].IEEE Tran. Power Delivery, 2003,18(4): 1350-1356.
  • 3柴旭峥,文习山,关根志,彭宁云.一种高精度的电力系统谐波分析算法[J].中国电机工程学报,2003,23(9):67-70. 被引量:140
  • 4T. Lobos, T. Kozina, H.J.koglin. Power systems harmonics estimation using linear least squares methods and SVD[C]. Proc. IEE Gen.Transmission Distrib. 2001,148(6): 567-572.
  • 5Z. Leonowicz, T. Lobos, P. Ruczewski, et al. Application of higher-order spectra for signal processing in electrical power engineering[C]. COMPEL 1998,17(5): 602-611.
  • 6Z. Leonowicz, T. Lobos, J. Rezmer. Advanced spectrum estimation methods for signal analysis in power electronics[J]. IEEE Tran.Industrial Electronics. 2003,50(3): 514-519.
  • 7任震,黄群古,黄雯莹,管霖.基于多频带小波变换的电力系统谐波分析新方法[J].中国电机工程学报,2000,20(12):38-41. 被引量:53
  • 8徐淑珍,刘晓冬,陈陈,朱子述.基于局部余弦变换的电力系统时变谐波分析新方法[J].中国电机工程学报,2001,21(12):12-15. 被引量:13
  • 9J.Rojo-Alvarez, A. Garcia-Alberola, M. Martinez_Ramon, et al.Support vector robust algorithms for nonparametric spectrum analysis[C]. Proc. ICANN,Madrid, Spain, 2002.
  • 10J.Rojo-Alvarez, M. Martinez_Ramon, M.Valdes, et al. A robust support vector algorithm for nonparametric spectral analysis[J]. IEEE Signal Processing Letters, 2003,10(11): 320-323.

二级参考文献56

  • 1谢明,丁康.频谱分析的校正方法[J].振动工程学报,1994,7(2):172-179. 被引量:130
  • 2崔锦泰 程正兴(译).小波分析导论[M].西安:西安交通大学出版社,1992..
  • 3边肇祺.模式识别[M].北京:清华大学出版社,1998..
  • 4孙仲康.快速傅立叶变换及其应用[M].人民邮电出版社,1982.200-203.
  • 5.GB7252-87.变压器油中溶解气体分析和判断导则[S].,..
  • 6.GB/T 17626.电磁兼容试验和测试技术供电系统及所连设备谐波、间谐波的测量和测量仪器导则[s].,1998..
  • 7Liu K. Comparison of very short-term load forecasting technique[J]. IEEE Trans. Power Systems, 1996,11(2): 877-882.
  • 8Hippert H S, Pefreira C E, Souza R C. Neural network for short-term load forecasting: A review and evaluation[J].IEEE Trans. Power System. 2001,16(2): 44-54.
  • 9Muller K R, Smola A J, Ratsch G, et al.Prediction time series with support vector machines[C].In Proc of ICANN'97., Springer LNCS 1327, Bedin,1997, 999-1004.
  • 10Papadakis S E, Theocharis J B, Kiartzis S J, et al. A novel approach to short-term load forecasting using fuzzy neural net-works[J].IEEE Trans. Power Systems, 1998,13(2):480-492.

共引文献1132

同被引文献491

引证文献60

二级引证文献596

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部