Abstract：In order to solve the problem of low accuracy of the low frequency oscillation modal parameter identification method in Gauss color noise and multi-channel signal, this paper presents a new method of resonance sparse signal decomposition (RSSD) used in the fault diagnosis of rolling bearings and independent component algorithm (ICA) applied to hybrid image separation are combined into the power system to realize low-frequency oscillation mode identification. The method uses RSSD to remove Gaussian color noise and transient impact to extract low-frequency oscillatory sustained signal , and then uses ICA to estimate the frequency and damping ratio of continuous signal. By comparing with ESPRIT and Prony methods, it is shown that this method can identify multi-channel signal parameters more quickly and accurately under the background of Gauss color noise and transient impact, and meet the requirements of identification of low-frequency oscillation in power system, so it has good application prospects.
刘君, 肖辉, 曾林俊, 江维. 基于RSSD和ICA算法的低频振荡模态参数辨识[J]. 电工技术学报, 2018, 33(21): 5051-5058.
Liu Jun, Xiao Hui, Zeng Linjun, Jiang Wei. Parameter Identification of Low Frequency Oscillation Based on RSSD and ICA Algorithm. Transactions of China Electrotechnical Society, 2018, 33(21): 5051-5058.
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2018, Vol. 33 
