| 引用本文: | 雷 强,刘光晔,朱永强,等.基于EEMD和矩阵束算法的低频振荡主导模式识别[J].电力系统保护与控制,2016,44(12):56-62.[点击复制] |
| LEI Qiang,LIU Guangye,ZHU Yongqiang,et al.Identification of the dominant mode based on EEMD and matrix pencil algorithm for low frequency oscillations[J].Power System Protection and Control,2016,44(12):56-62[点击复制] |
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| 摘要: |
| 传统矩阵束算法在低信噪比时，难以准确辨识出信号参数，误差较大。因此，结合集合经验模态分解(EEMD)和矩阵束算法，提出了一种电力系统低频振荡主导模式识别的新方法。该方法利用EEMD进行平稳化处理，通过互相关系数和信号能量权重找出含有主导模式的IMF分量，并利用矩阵束算法分析得到模态参数，从而扩展了传统矩阵束算法的应用范围。算例分析结果表明，该方法可以较好地适应非线性系统，抗噪声能力较强，在低信噪比时仍然可用于低频振荡主导模式的识别，为电力系统低频振荡问题的研究提供了新思路。 |
| 关键词: 矩阵束算法 低信噪比 集合经验模态分解 低频振荡 主导模式识别 |
| DOI：10.7667/PSPC151278 |
| 投稿时间：2015-07-23修订日期：2015-12-28 |
| 基金项目:国家自然科学基金资助项目(51577053) |
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| Identification of the dominant mode based on EEMD and matrix pencil algorithm for low frequency oscillations |
| LEI Qiang,LIU Guangye,ZHU Yongqiang,LIAO Tingjian |
| (School of Electrical and Information Engineering, Hunan University, Changsha 410082, China) |
| Abstract: |
| For the traditional matrix pencil algorithm, it is difficult to accurately identify the signal parameters and the error is bigger under low SNR. Therefore, a new method about dominant mode identification of low frequency in power systems is proposed, which combines ensemble empirical mode decomposition (EEMD) and matrix pencil algorithm. The signal is smoothed by using EEMD, and the cross-correlation coefficients and signal energy weighting ratio are used to select IMF which has main mode, and the above mode parameters are obtained by matrix pencil algorithm, which enlarge the scope of traditional matrix pencil algorithm. Analysis results show that, with adaption to nonlinear system and strong anti-noise ability, the method still can be used to identify the dominant mode of low frequency oscillation, providing a new idea to study low frequency oscillation in power system. This work is supported by National Natural Science Foundation of China (No. 51577053). |
| Key words: matrix pencil algorithm low SNR ensemble empirical mode decomposition low frequency oscillation dominant mode identification |
