关键词: 希尔伯特-黄变换, 经验模态分解, 端点效应, 极限学习机, B样条插值, 谐波分析

Abstract: In view of the end effect problems from the experience mode decomposition of Hilbert-Huang Transform process, this paper uses the Extreme Learning Machine（ELM） algorithm to extend the left and the right ends of the original data sequence respectively, takes the mean curve of the data sequence after extension through the B-spline interpolation method. Based on it, the Empirical Mode Decomposition（EMD） process starts, and then abandons both ends of extension data after decomposition is completed, so as to achieve the purpose of restrain end effect. By means of the contrast analysis of indicators of traditional neural network extension and support vector machine extension, the results show that, the algorithm not only can effectively restrain the end effect of EMD process but also get a great advantage in learning speed and decomposition accuracy. Applying the method to harmonic detection in power system, the simulation results show that this method can effectively inhibit the end effects of EMD and make a better decomposition of harmonic components of different frequencies contained in the harmonic signal.

Key words: Hilbert-Huang Transform（HHT）, Empirical Mode Decomposition（EMD）, end effect, Extreme Learning Machine（ELM）, B-spline function, harmonic analysis