期刊文献+

Synthetic PMU Data Creation Based on Generative Adversarial Network Under Time-varying Load Conditions

原文传递
导出
摘要 In this study,a machine learning based method is proposed for creating synthetic eventful phasor measurement unit(PMU)data under time-varying load conditions.The proposed method leverages generative adversarial networks to create quasi-steady states for the power system under slowly-varying load conditions and incorporates a framework of neural ordinary differential equations(ODEs)to capture the transient behaviors of the system during voltage oscillation events.A numerical example of a large power grid suggests that this method can create realistic synthetic eventful PMU voltage measurements based on the associated real PMU data without any knowledge of the underlying nonlinear dynamic equations.The results demonstrate that the synthetic voltage measurements have the key characteristics of real system behavior on distinct time scales.
出处 《Journal of Modern Power Systems and Clean Energy》 SCIE EI CSCD 2023年第1期234-242,共9页 现代电力系统与清洁能源学报(英文)
基金 supported by the National Science Foundation(No.OAC-1934675,No.ECCS-2035688,No.ECCS-1611301)。
  • 相关文献

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部