摘要
为了准确辨识电力系统次同步振荡模态参数,文章提出一种五点三次平滑和自回归滑动平均(auto-regressive moving average,ARMA)算法相结合的次同步振荡模态辨识方法。首先使用五点三次平滑算法对次同步振荡信号进行去噪预处理,然后对去噪后的信号建立ARMA模型进行次同步振荡模态参数辨识。算例分析结果表明,与希尔伯特黄变换(Hilbert-Huang transform,HHT)算法和ARMA算法相比,该方法去噪性能更好,辨识精度较高。进一步对仿真系统信号进行快速傅里叶变换(fast Fourier transform,FFT),其结果也验证了所提辨识方法的正确性和实用性。
In order to accurately identify subsynchronous oscillation modal parameter of power system,the subsynchronous oscillation mode identification method using the cubical smoothing algorithm with five-point approximation and auto-regressive moving average(ARMA)algorithm was proposed.The signal of subsynchronous oscillation was denoised by cubical smoothing algorithm with five-point approximation,and then the denoised signals were identified through establishing ARMA model to obtain the subsynchronous oscillation modal parameter.The results of example analysis show that the proposed method has better denoising performance and higher identification accuracy compared with Hilbert-Huang transform(HHT)algorithm and ARMA algorithm.The fast Fourier transform(FFT)results of the simulation system signal further verify the correctness and practicability of the proposed identification method.
作者
王雨虹
杨明昆
包伟川
付华
徐耀松
WANG Yuhong;YANG Mingkun;BAO Weichuan;FU Hua;XU Yaosong(School of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China;School of Safety Science and Engineering,Liaoning Technical University,Fuxin 123000,China;State Grid Fuxin Power Supply Company,Fuxin 123000,China)
出处
《合肥工业大学学报（自然科学版）》
CAS
北大核心
2020年第6期790-796,共7页
Journal of Hefei University of Technology：Natural Science
基金
国家自然科学基金资助项目(61601212)
辽宁省教育厅重点实验室资助项目(LJZS003)。
关键词
次同步振荡
五点三次平滑算法
ARMA算法
参数辨识
subsynchronous oscillation
cubical smoothing algorithm with five-point approximation
auto-regressive moving average(ARMA)algorithm
parameter identification
