低频振荡模态参数辨识的共振稀疏分解SSI分析方法

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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.

Key words： Low-frequency oscillation resonance-based sparse signal decomposition tunable Q-factor wavelet transform high-resonance component low-resonance component stochastic subspace identification

Received: 24 March 2014 Published: 03 March 2016

PACS:

TM712

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	Zhao Yan
	Li Zhimin
	Li Tianyun

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Zhao Yan,Li Zhimin,Li Tianyun. Low Frequency Oscillation Modal Parameter Identification Using Resonance-Based Sparse Signal Decomposition and SSI Method[J]. Transactions of China Electrotechnical Society, 2016, 31(2): 136-144.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I2/136

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