基于LSTM网络预测的水轮机机组运行状态检测

doi:10.6040/j.issn.1672-3961.0.2018.240

Abstract

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

CLC Number:

TP29

Chang CHEN,Xiaolei LI,Weiyu CUI. Hydraulic turbine operation status detection based on LSTM network prediction[J].Journal of Shandong University(Engineering Science), 2019, 49(3): 39-46.

Figures/Tables 12

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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

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

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

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)

时间序列长度	一元LSTM		多元LSTM
时间序列长度	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

Hydraulic turbine operation status detection based on LSTM network prediction

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