摘要
提出了基于精确模态阶数-指数型衰减正弦神经网络(EMO-EDSNN)的电力系统低频振荡模态辨识方法。首先,通过奇异值分解估计模态阶数。在关键的定阶问题上,采取EMO定阶方法,综合考虑了奇异值变化规律和奇异值本身大小2个因素,能够克服人为选取阈值的不足,提高阶数估计的准确性。然后,通过建立EDSNN将参数估计问题转化为优化问题求解。以输出信号和实测信号的平方误差最小为目标,并采用自适应的Levenberg-Marquardt算法训练神经网络收敛后,一次性计算出所有模态参数。最后,进行了数值信号仿真、EPRI-36系统仿真和实测信号仿真。仿真结果表明,所提方法能够快速准确地实现模态参数辨识。
The paper proposes a mode identification method for low frequency oscillations in power systems based on exact mode order-exponentially damped sinusoids neural network(EMO-EDSNN).Firstly,the mode order is estimated via singular value decomposition.An EMO method is employed to solve the key problem of order determination.It comprehensively considers the variation laws of singular values change and the values themselves,thus overcoming shortages of artificial thresholds and enhancing the accuracy of order determination.Secondly,the EDSNN is constructed to translate the parameter estimation into an optimization problem.After training the neural network via the self-adaptive Levenberg-Marquardt algorithm aiming for a minimum square error between output and real signals,all the mode parameters can be obtained simultaneously.Finally,simulations of numerical signals,EPRI-36 system and actual signals are carried out.The results show that the proposed method can identify the mode parameters in an accurate and reliable manner.
作者
丁仁杰
沈钟婷
DING Renjie;SHEN Zhongting(Department of Electrical Engineering of Tsinghua University,Beijing,00084,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2020年第3期122-131,共10页
Automation of Electric Power Systems
关键词
低频振荡
奇异值分解
指数衰减型正弦量
神经网络
模态辨识
low frequency oscillation
singular value decomposition
exponentially damped sinusoid
neural network
mode identification
