The advance in the wide-area measurement system (WAMS) is driving the power system to the trend of wide-area monitoring and control.The Prony method is usually used for low frequency oscillation online identification....The advance in the wide-area measurement system (WAMS) is driving the power system to the trend of wide-area monitoring and control.The Prony method is usually used for low frequency oscillation online identification.However,the identified amplitude and phase information is not sufficiently used.In this paper,the amplitude is adopted to detect the occurrence of the oscillation and to obtain the mode observability of the sites.The phase is adopted to identify the oscillation generator grouping and to obtain the mode shapes.The time varying characteristics of low frequency oscillations are studied.The behaviors and the characters of low frequency oscillations are displayed by dynamic visual techniques.Demonstrations on the "11.9" low frequency oscillation of the Guizhou Power Grid substantiate the feasibility and the validation of the proposed methods.展开更多
为解决低频振荡在电力系统中实测信号存在噪声干扰、信号处理过程中模态混叠及非线性问题,提出了基于小波无偏风险估计阈值消噪和变分模态分解-希尔伯特黄变化(VMD-HHT)的低频振荡分析的方法。首先,对于含噪的实测信号,采用小波无偏风...为解决低频振荡在电力系统中实测信号存在噪声干扰、信号处理过程中模态混叠及非线性问题,提出了基于小波无偏风险估计阈值消噪和变分模态分解-希尔伯特黄变化(VMD-HHT)的低频振荡分析的方法。首先,对于含噪的实测信号,采用小波无偏风险估计阈值进行消噪的预处理;其次,通过使用样本熵来确定VMD的二次惩罚因子、使用频谱图来确定分解层数,预处理后的信号经过VMD分解得到IMF(Intrinsic Mode Function)分量;最后,对得到的IMF分量进行希尔伯特黄变换得到模态的参数。通过复合信号测试和IEEE(Institute of Electrical and Electronics Engineers)四机二区域仿真的辨识结果,验证了所提方法的合理性和有效性。同时与TLS-ESPRIT算法、Prony算法和经验模态分解的结果分析对比可知,所提方法在辨识方面更为准确。展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 50595413)
文摘The advance in the wide-area measurement system (WAMS) is driving the power system to the trend of wide-area monitoring and control.The Prony method is usually used for low frequency oscillation online identification.However,the identified amplitude and phase information is not sufficiently used.In this paper,the amplitude is adopted to detect the occurrence of the oscillation and to obtain the mode observability of the sites.The phase is adopted to identify the oscillation generator grouping and to obtain the mode shapes.The time varying characteristics of low frequency oscillations are studied.The behaviors and the characters of low frequency oscillations are displayed by dynamic visual techniques.Demonstrations on the "11.9" low frequency oscillation of the Guizhou Power Grid substantiate the feasibility and the validation of the proposed methods.
文摘为解决低频振荡在电力系统中实测信号存在噪声干扰、信号处理过程中模态混叠及非线性问题,提出了基于小波无偏风险估计阈值消噪和变分模态分解-希尔伯特黄变化(VMD-HHT)的低频振荡分析的方法。首先,对于含噪的实测信号,采用小波无偏风险估计阈值进行消噪的预处理;其次,通过使用样本熵来确定VMD的二次惩罚因子、使用频谱图来确定分解层数,预处理后的信号经过VMD分解得到IMF(Intrinsic Mode Function)分量;最后,对得到的IMF分量进行希尔伯特黄变换得到模态的参数。通过复合信号测试和IEEE(Institute of Electrical and Electronics Engineers)四机二区域仿真的辨识结果,验证了所提方法的合理性和有效性。同时与TLS-ESPRIT算法、Prony算法和经验模态分解的结果分析对比可知,所提方法在辨识方面更为准确。
