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OACSTPCD
Localization method of subsynchronous oscillation source based on high-resolution time-frequency distribution image and CNN
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…查看全部>>
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 sour…查看全部>>
刘慧;成蕴丹;徐衍会;孙冠群;陈汝斯;余笑东
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
Subsynchronous oscillation source localizationSynchronous squeezing transformEnhanced short-time Fourier transformConvolutional neural networks
《全球能源互联网（英文）》 2024 (1)
1-13,13
This work was supported by the Science and Technology Project of State Grid Corporation of China(5100-202199536A-0-5-ZN).
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