考虑高斯有色噪声的FOMC-HTLS-Adaline算法在低频振荡模式辨识中的研究

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Abstract The colored Gaussian noises will affect the measured signals in wide area monitoring systems. Thus, a new identification method for low frequency oscillation modes based on FOMC-HTLS-Adaline was proposed. Firstly, with the advantages of blindness to Gaussian noise, four order mixed cumulants (FOMC) sequence replaced measure signals to identify low frequency oscillation modes. Secondly, Hankel total least squares (HTLS) and Adaline artificial neural network (ANN) estimated the frequency, attenuation factor, amplitude and phase of low frequency oscillation. The introduction of Adaline ANN solves the problem that amplitude and phase of modes are difficult to estimate after FOMC process, and reduces error accumulation of matrix calculation and improves the identification accuracy. The 4-machine two-area power system and measured phasor measurements units (PMU) both indicate that FOMC-HTLS-Adaline method accurately identifies low frequency oscillation modes under the circumstances with colored Gaussian noises.

Key words： Wide area monitoring system four order mixed cumulants colored gaussian noises Hankel total least squares Adaline artificial neural network modal information

Received: 18 April 2016 Published: 29 March 2017

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TM712

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	Wang Zhen
	Li Cheng
	Lin Zhifang
	Li Huizhang

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Wang Zhen,Li Cheng,Lin Zhifang等. Research on Low Frequency Oscillation Mode Identification Based on FOMC-HTLS-Adaline Algorithm Considering Colored Gaussian Noises[J]. Transactions of China Electrotechnical Society, 2017, 32(6): 21-30.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I6/21

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