摘要
研究低频振荡特征辨识算法是实现电力系统低频振荡监视的重要理论基础。文章基于Prony算法,结合变量投影(VP),采用奇异值分解(SVD),应用可分离最小二乘,辨识出Prony算法数学模型参数,不仅避免了传统Prony算法矩阵求逆困难和误差较大的问题,而且降低了参数空间的维数,简化了搜索空间的拓扑结构,增强了模式识别的抗噪性能和运算速度。仿真结果表明,该算法提高了电力系统低频振荡模式识别的效率和精度。
Low frequency oscillation characteristic analysis algorithms are used to monitor power sys-tem low frequency oscillation for the control of wide-area damping .In this paper ,the Prony method is integrated with variable projection （VP） approach ,the singular value decomposition （SVD） is used , and the separable nonlinear least squares are applied to identify the parameters of Prony mathematical model .The presented method not only avoids the problem in matrix inverse and larger error of the traditional Prony method ,but also reduces the dimension of the parameter space and simplifies the to-pology structure of the search space .It enhances the antinoise performance of mode identification and improves the computing speed .The simulation results show that the proposed method improves the accuracy and efficiency of the power system low frequency oscillation mode identification .
出处
《合肥工业大学学报（自然科学版）》
CAS
CSCD
北大核心
2014年第9期1045-1048,1054,共5页
Journal of Hefei University of Technology：Natural Science
基金
国家自然科学基金资助项目(61203106)
国家自然科学基金重点资助项目(50837001)
关键词
电力系统
PRONY算法
变量投影算法
低频振荡
奇异值分解
power system
Prony method
variable projection（VP） approach
low frequency oscilla-tion
singular value decomposition（SVD）
