基于时间序列的电力通信网络数据传输延时补偿算法

张立杨1; 李迅1; 王志伟1; 隗寿晨2; $22

基于时间序列的电力通信网络数据传输延时补偿算法

引用本文：张立杨1 ，李迅1 ，王志伟1 ，隗寿晨2，陈军2.基于时间序列的电力通信网络数据传输延时补偿算法[J].计算技术与自动化,2025,(4):38-43

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作者	单位
张立杨1 ，李迅1 ，王志伟1 ，隗寿晨2，陈军2	（1.国网山东省电力公司经济技术研究院，山东济南 250000；2.山东鲁软数字科技有限公司，山东济南 250000）

中文摘要:为了降低数据传输延迟，保证电力系统的稳定运行，研究了基于时间序列的电力通信网络数据传输延时补偿算法。以TCN网络分析采集通信延迟数据为样本输入，利用果蝇算法优化长短期神经网络算法实现了电力通信网络数据的延迟预测，并以预测结果为输出，利用卡尔曼滤波器完成了数据延迟补偿与校正。实验结果表明，改进后的LSTM时间序列模型预测精度高，延时补偿准确性高，误差小于0.001，延时时间更短，补偿效果更明显。

中文关键词:数据传输延迟果蝇算法卡尔曼滤波器 LSTM时间序列模型

Data Transmission Delay Dompensation Algorithm of Power Communication Network Based on Time Series

Abstract:In order to reduce the data transmission delay and ensure the stable operation of the power system, this paper studies the time-series-based data transmission delay compensation algorithm of the power communication network. With the communication delay data collected by TCN network analysis as the sample input, the fruit fly algorithm is used to optimize the long-term and short-term neural network (Long Short-Term Memory,LSTM). LSTM algorithm realizes the delay prediction of power communication network data, and takes the prediction result as output, and completes the data delay compensation and correction by Kalman filter. The experimental results show that the improved LSTM time series model has high prediction accuracy and high delay compensation accuracy, the error is less than 0.001, the delay time is shorter, and the compensation effect is more obvious.

keywords:data transmission delay drosophila algorithm Kalman filter LSTM time series model

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