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
摘要：随着电网规模扩大和电力元件不断增加，电力系统检修方式变得日趋复杂，仅依靠传统方法难以对海量检修方式下电网的低频振荡风险进行评估。针对此问题，提出了一种基于长短期记忆(long short term memory，LSTM)神经网络的检修态电网低频振荡风险预测方法。首先，提出了电力系统检修方式的统一编码方法，使计算机能够快速、准确识别电网在各种检修方式下的运行状态；然后，基于同步相量测量单元(phasor measurement unit，PMU)实时测量的电网历史运行数据，利用LSTM神经网络对不同检修方式下电网的低频振荡次数进行预测，从而评估检修态电网发生低频振荡的风险；最后，以华中地区某省级电网为算例，验证了所提方法的准确性和快速性。
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.
标题：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
title:Risk Prediction Method of Low Frequency Oscillation in Maintenance Power Network Based on Long Short Term Memory Neural Network
作者：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
authors:Hongjun FU, Shaoxuan ZHU, Buhua WANG, Yan XIE, Haoqing XIONG, Xiaojun TANG, Xiaoyong DU, Chenghao LI, Xiaomeng LI
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),
keywords:power system,maintenance method,computer coding,low frequency oscillation,risk prediction,long short term memory (LSTM),
发表日期：2024-04-30
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