电工技术学报2017，Vol.32Issue(6)：21-30,10.
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
Research on Low Frequency Oscillation Mode Identification Based on FOMC-HTLS-Adaline Algorithm Considering Colored Gaussian Noises
王臻 1李承 1林志芳 1李惠章1
作者信息
- 1. 华中科技大学电气与电子工程学院 武汉 430074
- 折叠
摘要
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.关键词
广域测量系统/四阶混合累积量/高斯色噪声/HTLS/Adaline人工神经网络/模态信息Key words
Wide area monitoring system/four order mixed cumulants/colored gaussian noises/Hankel total least squares/Adaline artificial neural network/modal information分类
信息技术与安全科学引用本文复制引用
王臻,李承,林志芳,李惠章..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[J].电工技术学报,2017,32(6):21-30,10.
