Power System Low Frequency Oscillation Mode Identification Based on Exact Mode Order-Exponentially Damped Sinusoids Neural Network

被引：0

作者：

Ding R. ^{[1
]}

Shen Z. ^{[1
]}

机构：

[1] Department of Electrical Engineering of Tsinghua University, Beijing

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2020年 / 44卷 / 03期

关键词：

Exponentially damped sinusoid; Low frequency oscillation; Mode identification; Neural network; Singular value decomposition;

D O I：

10.7500/AEPS20190321005

中图分类号：

学科分类号：

摘要：

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. © 2020 Automation of Electric Power Systems Press.

引用

页码：122 / 131

页数：9

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