Trend Prediction Method of Power Network Dynamic Trajectory Based on Long Short Term Memory Neural Networks

被引：0
|
作者：
Yang S. [1 ]
Liu D. [2 ]
An J. [1 ]
Li Z. [2 ]
Yang H. [2 ]
Zhao G. [2 ]
机构：
[1] School of Electrical Engineering, Northeast Dianli University, Jilin, 132012, Jilin Province
[2] China Electric Power Research Institute, Haidian District, Beijing
来源：
Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2020年 / 40卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Artificial intelligence; Emergency control; Homotaxis identification; Long-short term memory; Voltage phase trajectory;
D O I：
10.13334/j.0258-8013.pcsee.190570
中图分类号：
学科分类号：
摘要：
In order to meet the requirement of speed and precision for active defense of large power grid, a prediction method based on long short term memory for dynamic trajectory of power network was proposed in this paper. Firstly, aiming at the geometric characteristics of the track of the voltage sequential phasor, the time sequence evolution rule of the node state was extracted, and the convergence of the generator motion was identified quickly. Secondly, based on the long short term memory, the disturbed trajectory of the equivalent two-machine system was predicted rapidly. Finally, the emergency control of transient power angle stability was realized by calculating the cutting capacity according to the extended equal area criterion (EEAC). An example of IEEE 39 system shows the effectiveness of the proposed method. This method does not require complex computation and is time-consuming and has a good application value in engineering. © 2020 Chin. Soc. for Elec. Eng.
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页码：2854 / 2865
页数：11
相关论文
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