Low Frequency Oscillation Modal Parameter Identification Using Resonance-Based Sparse Signal Decomposition and SSI Method
Zhao Yan1, 2, Li Zhimin1, Li Tianyun2
1. School of Electrical and Automation Engineering Harbin Institute of Technology Harbin 150001 China; 2. Northeast Dianli University Jilin 132012 China
Abstract：This paper proposed a new method based on resonance-based sparse signal decom- position and stochastic subspace identification (SSI) for oscillation mode identification. Complex signals can be separated by predictable Q-factors. Firstly, LFO signals were decomposed into high-resonance component, low-resonance component and residual by resonance-based sparse signal decomposition. LFO signal is the output of under-damped system with high-resonance property at a specific frequency. The high-resonance component is extractive LFO, and the residual is the most colored Gaussian noise. Secondly, modal parameter of high-resonance component is identified by SSI. After that, high-accuracy detection for modal parameter identification is achieved. Examples have proved the effectiveness of the method.
赵妍, 李志民, 李天云. 低频振荡模态参数辨识的共振稀疏分解SSI分析方法[J]. 电工技术学报, 2016, 31(2): 136-144.
Zhao Yan, Li Zhimin, Li Tianyun. Low Frequency Oscillation Modal Parameter Identification Using Resonance-Based Sparse Signal Decomposition and SSI Method. Transactions of China Electrotechnical Society, 2016, 31(2): 136-144.
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