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作 者:张虹[1] 勇天泽 王迎丽 苏欣 代宝鑫 ZHANG Hong;YONG Tianze;WANG Yingli;SU Xin;DAI Baoxin(School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,China;Yingkou Power Supply Company,State Grid Liaoning Electric Power Supply Co.,Ltd,Yingkou 115000,China)
机构地区:[1]东北电力大学电气工程学院,吉林132012 [2]国网辽宁省电力有限公司营口市供电公司,营口115000
出 处:《电力系统及其自动化学报》2020年第7期18-24,共7页Proceedings of the CSU-EPSA
摘 要:随着水电机组在电力系统渗透率的不断提高,低频振荡和超低频振荡等振荡形式有时会同时存在。针对多类型振荡模态信号特征参数辨识不精准的问题,文中采用自适应变分模态分解VMD(variational mode decomposition)算法和Hilbert变换对振荡信号进行特征参数辨识。利用能量损失系数确定模态数量,根据惩罚因子与模态分量中心频率的映射关系确定多匹配惩罚因子,自适应地对不同频段的振荡信号进行准确的模态分解,结合Hilbert变换对每组模态进行特征参数辨识。仿真算例和实测数据分析结果表明:本文方法自适应性强,与Prony滑动窗算法和ESPRIT算法相比,特征参数的辨识精度具有较大的提高。With the increasing penetration of hydropower units in power systems,oscillations such as low-frequency and ultra-low frequency ones sometimes coexist simultaneously. For the problem of inaccurate characteristic parameter identification with the multi-type oscillation modal signals,the adaptive variational mode decomposition(VMD)algorithm and Hilbert transform are adopted to identify the characteristic parameters of oscillation signals. An energy loss coefficient is used to determine the number of modes,and the multiple matching penalty factor is determined according to the mapping relationship between the penalty factor and the modal component’s center frequency,thus adaptively performing accurate mode decomposition on the oscillation signals in different frequency bands. In addition,the characteristic parameter identification is performed on each group of modes with the combination of Hilbert transform. The results of simulation examples and analysis of measured data show that the proposed method has strong adaptability,and its identification accuracy for characteristic parameters is obviously improved compared with the Prony moving window algorithm and the ESPRIT algorithm.
关 键 词:多类型振荡 自适应变分模态分解 能量损失系数 多匹配惩罚因子 HILBERT变换
分 类 号:TV712[水利工程—水利水电工程]
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