基于LSTM的滚动预测算法的电缆缆芯温度的研究
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上海电力大学自动化工程学院 上海 200090

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TM762

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Power cable; Core temperature; Rolling LSTM network; Temperature prediction


Research on cable core temperature based on rolling prediction algorithm of LSTM
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School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

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    摘要:

    在电力系统实际工作的过程中,电缆导体的温度过高往往会造成电力系统出现故障,但由于电缆的缆芯温度不易监测,因此提出一种基于LSTM(Long Short-Term Memory)的滚动预测方法对电缆的缆芯温度进行预测。根据采集到的缆芯温度数据集,利用该算法对模型进行训练,动态调节网络模型参数,学习数据变化的规律,从而实现缆芯温度的预测。结果表明该算法模型的RMSE为0.1979℃,与BP、LSTM算法模型进行对比,验证了该算法模型可以有效的预测短期缆芯温度变化趋势,表明该算法在电力系统安全运行方面具有一定的实际应用意义。

    Abstract:

    In the actual operation of power system, the high temperature of cable conductor often causes power system failure, but because the cable core temperature is not easy to monitor, therefore, a rolling prediction method based on LSTM (Long Short-Term Memory) is proposed to predict the cable core temperature. According to the collected cable core temperature data set, this algorithm is used to train the model, dynamically adjust the parameters of the network model, and study the law of the data change, so as to realize the prediction of cable core temperature. The results show that the RMSE of the algorithm model is 0.1979 °C, and compared with the BP and LSTM algorithm model, the algorithm model can effectively predict the short-term cable core temperature change trend, the results show that the algorithm has practical significance in the safe operation of power system.

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孙俊峰,李志斌.基于LSTM的滚动预测算法的电缆缆芯温度的研究[J].电子测量技术,2021,44(21):84-88

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  • 在线发布日期: 2024-07-08
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