发电技术2024，Vol.45Issue(2)：353-362,10.DOI:10.12096/j.2096-4528.pgt.22152
基于长短期记忆神经网络的检修态电网低频振荡风险预测方法
Risk Prediction Method of Low Frequency Oscillation in Maintenance Power Network Based on Long Short Term Memory Neural Network
摘要
Abstract
With the expansion of power grid scale and the increase of power components,the maintenance methods of power system become more and more complex.It is difficult to evaluate the low-frequency oscillation risk of power grid under massive maintenance only by traditional methods.To solve this problem,a risk prediction method of low-frequency oscillation in maintenance power network based on long short term memory(LSTM)neural network was proposed.Firstly,the unified coding method of power system maintenance mode was proposed,so that the computer can quickly and accurately identify the operation state of power grid under various maintenance modes.Then,based on the historical data measured in real time by phasor measurement unit(PMU),the number of low-frequency oscillation of power grid under different maintenance modes was predicted by using LSTM neural network,so as to evaluate the risk of low-frequency oscillation of power grid under maintenance.Finally,a regional power grid in central China was taken as an example to verify the accuracy and rapidity of the proposed method.关键词
电力系统/检修方式/计算机编码/低频振荡/风险预测/长短期记忆(LSTM)Key words
power system/maintenance method/computer coding/low frequency oscillation/risk prediction/long short term memory(LSTM)分类
能源与动力引用本文复制引用
付红军,朱劭璇,王步华,谢岩,熊浩清,唐晓骏,杜晓勇,李程昊,李晓萌..基于长短期记忆神经网络的检修态电网低频振荡风险预测方法[J].发电技术,2024,45(2):353-362,10.基金项目
国网河南省电力公司科技项目(5217022000A8). Project Supported by Science and Technology Foundation of State Grid Henan Electric Power Company(5217022000A8). （5217022000A8）
