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山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (3): 39-46.doi: 10.6040/j.issn.1672-3961.0.2018.240

• 控制科学与工程 • 上一篇    下一篇

基于LSTM网络预测的水轮机机组运行状态检测

陈畅(),李晓磊*(),崔维玉   

  1. 山东大学控制科学与工程学院, 山东 济南 250061
  • 收稿日期:2018-06-06 出版日期:2019-06-20 发布日期:2019-06-27
  • 通讯作者: 李晓磊 E-mail:sduccchenchang@163.com;qylxl@sdu.edu.cn
  • 作者简介:陈畅(1995—),女,吉林四平人,硕士研究生,主要研究方向为基于大数据分析的设备故障预测方法研究. E-mail: sduccchenchang@163.com

Hydraulic turbine operation status detection based on LSTM network prediction

Chang CHEN(),Xiaolei LI*(),Weiyu CUI   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2018-06-06 Online:2019-06-20 Published:2019-06-27
  • Contact: Xiaolei LI E-mail:sduccchenchang@163.com;qylxl@sdu.edu.cn

摘要:

利用长短期记忆(long short-term memory, LSTM)网络对水轮机机组的运行状态进行预测。对水轮机机组的流式监测数据进行标准化处理,并利用滑动窗口技术将数据转换为LSTM网络训练所需的训练数据集与测试数据集;给出LSTM预测模型结构,并通过调节网络层数、隐层神经元数目等参数对模型进行优化,建立水轮机机组的时间序列数据预测模型。经试验分析验证,与其它模型相比,基于多测点的多元长短期记忆网络预测模型具备更高的预测精度,并基于改进的雷达图分析法计算健康偏离度,成功地检测出某水电厂5号水轮机机组5月末的数据出现异常,验证了模型的有效性。

关键词: 水轮机, 数据预测, 长短期记忆网络

Abstract:

Long short-term memory (LSTM) networks was adapted to make accurate prediction of the unit's operation status. The streaming monitoring data of the turbine unit was standardized, and the sliding window technology was used to convert the data into the training data set and test data set for LSTM network training. The LSTM prediction model structure was given, and the structure of LSTM prediction model was fine-tuned, such as the number of network layers and the number of hidden layer neurons. The time series data prediction model of the hydro turbine unit was established. The experimental analysis proved that the multi-measurement-based LSTM network prediction model had higher prediction accuracy than other models, which calculated the health deviation based on the improved radar image analysis method and successfully detected the abnormality of the No. 5 hydraulic turbine unit of a hydropower plant at the end of May, and verified the validity of the model.

Key words: hydraulic turbine, data forecasting, long short-term memory networks

中图分类号: 

  • TP29

图1

RNN结构图"

图2

LSTM单元结构图"

图3

LSTM 5层网络结构"

图4

LSTM 3层网络结构"

表1

网络层数性能比较"

网络层数 MSE 编译时间/s 训练时间/s
5 0.026 8 0.046 0 2 007.956
3 0.072 2 0.078 0 1 131.730

表2

LSTM预测性能比较"

节点数 MSE 计算时间/s
25 0.135 973.77
30 0.174 1 039.30
35 0.034 1 201.15
40 0.089 1 231.96
50 0.177 1 363.47

表3

基于三种算法的残差测试对比试验结果"

算法 残差均值 残差方差
SGD 0.136 830 0.026 786
Adagrad 0.112 304 0.026 312
RMSprop 0.119 042 0.027 368

表4

数据格式"

训练(测试集) 普通神经网络 RNN 一元LSTM 多元LSTM
Train_X (72220, 20) (72220, 20, 1) (72220, 20, 1) (72220, 20, 6)
Train_Y (72220, 1) (72220, 1) (72220, 1) (72220, 6)
Test_X (3800, 20) (3800, 20, 1) (3800, 20, 1) (3800, 20, 6)
Test_Y (3800, 1) (3800, 1) (3800, 1) (3800, 1)

图5

四种预测模型的结果对比"

表5

一元LSTM与多元LSTM的均方误差与平均绝对误差对比"

时间序列
长度
一元LSTM 多元LSTM
MSE MAD MSE MAD
5 0.057 3 0.028 5 0.022 0 0.017 0
10 0.037 7 0.019 9 0.017 1 0.008 7
15 0.038 9 0.017 6 0.005 3 0.005 6
20 0.039 8 0.012 0 0.006 8 0.008 1
25 0.044 3 0.037 4 0.011 9 0.019 2

图6

四种预测模型的结果对比"

图7

健康偏离度曲线"

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