全球能源互联网（英文）2024，Vol.7Issue(1)：1-13,13.DOI:10.1016/j.gloei.2024.01.001
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
Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN
摘要
Abstract
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.关键词
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Key words
Subsynchronous oscillation source localization/Synchronous squeezing transform/Enhanced short-time Fourier transform/Convolutional neural networks引用本文复制引用
刘慧,成蕴丹,徐衍会,孙冠群,陈汝斯,余笑东..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[J].全球能源互联网（英文）,2024,7(1):1-13,13.基金项目
This work was supported by the Science and Technology Project of State Grid Corporation of China(5100-202199536A-0-5-ZN). （5100-202199536A-0-5-ZN）
