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Turbine data cleaning based on deep LSTM
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Xiaogang XU1, 2, 3, Zhixiang WANG1, Huijie WANG1, 2, 3
Thermal Power Generation | 2023, 52(8) : 179 - 187
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Thermal Power Generation | 2023, 52(8): 179-187
• Power generation technology forum •
Turbine data cleaning based on deep LSTM
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Xiaogang XU1, 2, 3, Zhixiang WANG1, Huijie WANG1, 2, 3
Affiliations
  • 1.Department of Power Engineering, North China Electric Power University, Baoding 071003, China
  • 2.Key Laboratory of Low Carbon and Efficient Power Generation Technology of Hebei, North China Electric Power University, Baoding 071003, China
  • 3.Baoding Key Laboratory of Low Carbon and Efficient Power Generation Technology, North China Electric Power University, Baoding 071003, China
Published: 2023-08-25 doi: 10.19666/j.rlfd.202210213
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A large amount of data is generated during steam turbine operation. In order to meet the requirements of high quality data driven by big data and simulation modeling, efficient data cleaning is very necessary. The semi-supervised data cleaning model of steam turbine is built by using the excellent nonlinear fitting ability of long and short memory layer for time series data. The model selects three boundary conditions of the unit as input to predict the cleaning data. Outliers are eliminated according to the residual difference between the predicted value and the actual value. Then, the predicted value of the model is used to fill the data to ensure the integrity of the data. The model is used to clean the data of a 650 MW unit in a power plant. To overcome the problems caused by sample imbalance in the selection of cleaning model indicators, the accuracy rate is improved and taken as the measurement index of cleaning effect. The results show that, the improved accuracy of the data cleaning model of the deep long and short memory network is higher than that of the other three common cleaning methods, which can effectively identify whether the data is abnormal, and can use the predicted value to fill the data to ensure the consistency of data before and after cleaning.

long and short memory networks  /  deep learning  /  data cleaning  /  outliers  /  steam turbine
Xiaogang XU, Zhixiang WANG, Huijie WANG. Turbine data cleaning based on deep LSTM[J]. Thermal Power Generation, 2023 , 52 (8) : 179 -187 . DOI: 10.19666/j.rlfd.202210213
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  • Fundamental Research Funds for the Central Universities(2019MS094)
Appendix
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Year 2023 volume 52 Issue 8
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58
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Article Info
doi: 10.19666/j.rlfd.202210213
  • Online Date：2026-01-26
  • Published：2023-08-25
Article Data
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History
  • Revised：2022-10-18
Funding
Fundamental Research Funds for the Central Universities(2019MS094)
Affiliations
    1.Department of Power Engineering, North China Electric Power University, Baoding 071003, China
    2.Key Laboratory of Low Carbon and Efficient Power Generation Technology of Hebei, North China Electric Power University, Baoding 071003, China
    3.Baoding Key Laboratory of Low Carbon and Efficient Power Generation Technology, North China Electric Power University, Baoding 071003, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202210213
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