| 基于时间序列的电力通信网络数据传输延时补偿算法 |
投稿时间：2024-03-05 修订日期：2024-05-09  点此下载全文 |
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| 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,该研究建立了数据传输系统,以TCN网络分析采集通信延迟数据为样本输入,靠果蝇算法优化长短期神经网络(Long Short-Term Memory,LSTM)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,经过卡尔曼滤波后本文方法延时补偿准确性高,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 |
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| Data transmission delay compensation algorithm of power communication network based on time series |
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| Abstract:In order to reduce the data transmission delay and ensure the stable operation of the power system, this study established a data transmission system, and took the communication delay data collected by TCN network analysis as the sample input, and optimized the Long Short-Term Memory (LSTM) algorithm by the fruit fly algorithm to realize the delay prediction of the power communication network data. With the predicted results as output, the data delay compensation and correction are completed by using Kalman filter. The experimental results show that the improved LSTM time series model has high prediction accuracy, and the proposed method has high delay compensation accuracy after Kalman filtering, 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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