基于RSSD和ICA算法的低频振荡模态参数辨识

doi:10.19595/j.cnki.1000-6753.tces.171465

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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.

Key words： Low-frequency oscillation modal parameter identification Gauss color noise multi-channel signal resonance sparse signal decomposition independent component algorithm

Received: 26 October 2017 Published: 12 November 2018

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TM712

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	Liu Jun
	Xiao Hui
	Zeng Linjun
	Jiang Wei

Cite this article:

Liu Jun,Xiao Hui,Zeng Linjun等. Parameter Identification of Low Frequency Oscillation Based on RSSD and ICA Algorithm[J]. Transactions of China Electrotechnical Society, 2018, 33(21): 5051-5058.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171465 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I21/5051

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