基于EMO-EDSNN的电力系统低频振荡模态辨识

丁仁杰; 沈钟婷

doi:10.7500/AEPS20190321005

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基于EMO-EDSNN的电力系统低频振荡模态辨识

学术研究 | 更新时间：2025-06-16

- 基于EMO-EDSNN的电力系统低频振荡模态辨识
- Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network
- 电力系统自动化-方正平台 2020年44卷第3期页码：122-131
- 作者机构：
  
  清华大学电机工程与应用电子技术系,北京市 100084
- 作者简介：
  
  丁仁杰(1957—),男,博士,副教授,主要研究方向:电力系统稳定与控制。E-mail：renjied@tsinghua.edu.cn
  沈钟婷(1994—),女,通信作者,硕士研究生,主要研究方向:电力系统稳定与控制。E-mail：jocelynshen1994@126.com
- 基金信息：
- DOI：10.7500/AEPS20190321005
  中图分类号：
- 收稿日期：2019-03-21，
  
  修回日期：2019-06-14，
  
  网络出版日期：2019-10-29，
  
  纸质出版日期：2020-03-25
- 稿件说明：
移动端阅览
丁仁杰,沈钟婷.基于EMO-EDSNN的电力系统低频振荡模态辨识[J].电力系统自动化,2020,44(03):122-131.

DING Renjie,SHEN Zhongting.Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network[J].Automation of Electric Power Systems,2020,44(03):122-131.
丁仁杰,沈钟婷.基于EMO-EDSNN的电力系统低频振荡模态辨识[J].电力系统自动化,2020,44(03):122-131. DOI： 10.7500/AEPS20190321005.

DING Renjie,SHEN Zhongting.Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network[J].Automation of Electric Power Systems,2020,44(03):122-131. DOI： 10.7500/AEPS20190321005.

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。

Abstract

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.

关键词

Keywords

references

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