| 基于自然激励技术和TLS-ESPRIT方法的低频振荡模式辨识 |
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| 引用本文： | 王祥超,张鹏,甄威,王晓茹.基于自然激励技术和TLS-ESPRIT方法的低频振荡模式辨识[J].电力系统自动化,2015,39(10):75-80. |
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| 作者姓名： | 王祥超 张鹏 甄威 王晓茹 |
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| 作者单位： | 1. 西南交通大学电气工程学院,四川省成都市,610031
2. 四川电力科学研究院,四川省成都市,610072 |
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| 摘 要： | 提出了一种利用系统随机响应信号辨识低频振荡模式的方法。利用自然激励技术(NEx T)从系统随机响应信号中获得自由振荡信号,采用总体最小二乘—旋转不变技术的信号参数估计(TLS-ESPRIT)方法对所获得的自由振荡信号进行模式辨识,得到低频振荡模式的频率和阻尼比。采用16机系统仿真数据和四川电网实测数据验证了所提方法的有效性;与随机减量技术(RDT)结合Prony方法对比表明,所提方法对阻尼比估计更准确且抗噪性更强。
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| 关 键 词： | 低频振荡 参数辨识 随机响应 自然激励技术 |
| 收稿时间： | 2014/6/24 0:00:00 |
| 修稿时间： | 2014/11/6 0:00:00 |
Identification of Low Frequency Oscillation Modes Based on NExT and TLS-ESPRIT Algorithm |
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| WANG Xiangchao,ZHANG Peng,ZHEN Wei and WANG Xiaoru.Identification of Low Frequency Oscillation Modes Based on NExT and TLS-ESPRIT Algorithm[J].Automation of Electric Power Systems,2015,39(10):75-80. |
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| Authors: | WANG Xiangchao ZHANG Peng ZHEN Wei and WANG Xiaoru |
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| Affiliation: | School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China,School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China,Sichuan Electric Power Research Institute, Chengdu 610072, China and School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China |
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| Abstract: | An approach to identifying low frequency oscillation modes based on the stochastic response signals of the system is presented. First, the free oscillation signals are extracted from the stochastic response signals of the system by the natural excitation technique (NExT). Then the mode parameters of the obtained free oscillation signals are identified by the total least squares-estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) algorithm. Finally, the frequencies and damping ratios of low frequency oscillations are obtained. The effectiveness of the proposed method has been verified by the simulation data from a 16-machine system and the measurement data in Sichuan power grid. Compared with the random decrement technique (RDT) and Prony method, the proposed method is more accurate in identifying the damping ratios and has a better noise immunity performance. |
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| Keywords: | low frequency oscillation parameter identification stochastic response natural excitation technique (NExT) |
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