首页|基于高分辨率时频分布图像和卷积神经网络的次同步振荡源定位方法

基于高分辨率时频分布图像和卷积神经网络的次同步振荡源定位方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
随着风电等新能源渗透率的不断提高，次同步振荡事件愈发频繁，需要进一步研究快速、准确的次同步振荡定位方法，实现实际工程中次同步振荡抑制。本文提出了一种基于改进短时傅里叶变换和卷积神经网络的次同步振荡源定位方法，该方法可以考虑风电场的随机波动性，实现次同步振荡源在线定位。首先，基于机组端口量测数据进行改进短时傅里叶变换获取高分辨率时频分布图像;其次，组合各机组端口时频分布图像，生成次同步振荡特征图作为卷积神经网络的输入，基于历史数据进行卷积神经网络训练;最后，通过训练完成的次同步振荡定位模型实现次同步振荡源在线定位。基于PSCAD/EMTDC平台搭建多机系统模型验证了所提方法的有效性和准确性。
Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN
The penetration of new energy sources such as wind power is increasing,which consequently increases the occurrence rate of subsynchronous oscillation events.However,existing subsynchronous oscillation source-identification methods primarily analyze fixed-mode oscillations and rarely consider time-varying features,such as frequency drift,caused by the random volatility of wind farms when oscillations occur.This paper proposes a subsynchronous oscillation source-localization method that involves an enhanced short-time Fourier transform and a convolutional neural network(CNN).First,an enhanced STFT is performed to secure high-resolution time-frequency distribution(TFD)images from the measured data of the generation unit ports.Next,these TFD images are amalgamated to form a subsynchronous oscillation feature map that serves as input to the CNN to train the localization model.Ultimately,the trained CNN model realizes the online localization of subsynchronous oscillation sources.The effectiveness and accuracy of the proposed method are validated via multimachine system models simulating forced and natural oscillation events using the Power Systems Computer Aided Design platform.Test results show that the proposed method can localize subsynchronous oscillation sources online while considering unpredictable fluctuations in wind farms,thus providing a foundation for oscillation suppression in practical engineering scenarios.

Subsynchronous oscillation source localizationSynchronous squeezing transformEnhanced short-time Fourier transformConvolutional neural networks

刘慧、成蕴丹、徐衍会、孙冠群、陈汝斯、余笑东

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Institute of Electric Power Systems,School of Electrical and Electronic Engineering,North China Electric Power University,Beijing 102206,P.R.China

Electric Power Research Institute of State Grid Hubei Electric Power Co.,Ltd.,Wuhan 430077,P.R.China

Central China Branch of State Grid Corporation of China,Wuhan 430077,P.R.China

次同步振荡源定位 同步挤压变换 改进短时傅里叶变换 卷积神经网络

Science and Technology Project of State Grid Corporation of China

5100-202199536A-0-5-ZN

2024

10.1016/j.gloei.2024.01.001

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全球能源互联网(英文)

CSTPCDEI
ISSN：2096-5117
年,卷(期)：2024.7(1)
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