基于时序分析的阀冷进阀温度预测方法
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江苏高校“青蓝工程”; 中央高校基本科研业务项目(NS2019055)


Prediction of Inlet Valve Temperature Based on Time Series Analysis
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    摘要:

    电网在运行过程中, 换流阀等关键设备会不断产生热量, 当设备的热量不断聚集温度不断上升, 会影响设备的稳定性和安全性, 保证换流阀等关键设备稳定运行就显得至关重要. 阀冷系统作为冷却系统的关键设备, 以热导性较高的水为介质, 将设备的热能带出, 达到降低设备温度的目的. 可以通过监控冷却水的温度、压力技术指标来确保换流阀安全、稳定运行. 选取阀冷系统中的进阀温度为主要预测指标, 对系统的历史数据进行充分的挖掘和分析, 达到对电网运行状态预估的目的. 将传统时序模型与机器学习结合提出ARIMA-SVM的混合模型, 并与传统的 ARIMA 模型、SVM模型和GRU神经网络模型对中国南方电网的真实阀冷数据进行时序分析预测并进行对比实验. 实验结果表明, ARIMA模型、SVM模型、GRU神经网络模型和ARIMA-SVM混合模型都可以较好地预测进阀温度的变化趋势, 但ARIMA-SVM混合模型在均方根误差、均方误差和平均绝对误差3个评价指标上表现均更优于其他3个模型, 能够进一步提升进阀温度预测的精度

    Abstract:

    During the operation of the power grid, key devices such as converter valves continue to generate heat, affecting the stability and safety of the system. Then it is crucial to ensure the stable operation of those devices. As a major component in the cooling system, the valve cooling system releases the heat energy from the equipment with water of high thermal conductivity as the medium. The stable operation of the converter valve can be ensured by monitoring the temperature and pressure of the cooling water. Also, with inlet valve temperature in the valve cooling system as the main predictive index, the historical data of the system is fully mined for predicting the operating state of the power grid. An ARIMA-SVM hybrid model integrating the traditional time series model and machine learning is compared with the traditional ARIMA model, the SVM model and the GRU neural network model with regard to the time series analysis of the real valve cooling data from China Southern Power Grid. The comparative experimental results demonstrate that the above four models can all clearly indicate the trend of the inlet valve temperature. However, the ARIMA-SVM hybrid model behaves better in the evaluation indicators including the root mean square error, the mean square error and the mean absolute error than the other three, with a more accurate prediction of inlet valve temperature.

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张朝辉,梁家豪,梁秉岗,秦冠军,尹毅然,丁笠,周宇.基于时序分析的阀冷进阀温度预测方法.计算机系统应用,2021,30(4):9-16

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  • 收稿日期:2020-08-25
  • 最后修改日期:2020-09-15
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  • 在线发布日期: 2021-03-31
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