Low frequency oscillation modal parameter identification using resonance-based sparse signal decomposition and SSI method

This paper proposed a new method based on resonance-based sparse signal decomposition 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.