Home Journals Articles Updates About
CN
image
收藏切换
A Health Performance Tendency Prediction Model of Pumped Storage Unit Based on Convolution Neural Network-long Short-term Memory Neural Network
收藏切换
PDF
Ya-hui SHAN1, Hao WANG1, Gen-ping WU1, Jie LIU2
Water Resources and Power | 2023, 41(8) : 185 - 187
Less
收藏切换
Water Resources and Power | 2023, 41(8): 185-187
• ELECTROMECHANICS AND CONTROL ENGINEERING •
A Health Performance Tendency Prediction Model of Pumped Storage Unit Based on Convolution Neural Network-long Short-term Memory Neural Network
Full
Ya-hui SHAN1, Hao WANG1, Gen-ping WU1, Jie LIU2
Affiliations
  • 1.Wuhan Second Ship Design and Research Institute, Wuhan 430064, China
  • 2.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Published: 2023-08-25 doi: 10.20040/j.cnki.1000-7709.2023.20230182
Outline
收藏切换

To accurately obtain the health performance level of a pumped storage unit (PSU), a health performance tendency prediction method based on convolution neural network-long short-term memory neural network (CNN-LSTM) is proposed. Firstly, a unit health state model based on Gaussian process regression was constructed to effectively characterize the operating characteristics of the PSU. Then, an index that can quantify the health performance of the PSU was proposed. Finally, by integrating the good local feature extraction ability of the CNN and the advantage of the LSTM in time series prediction, a prediction model based on CNN-LSTM was proposed. The experiments were conducted using monitoring data from a pumped storage station in China. The results show that the proposed method can betterly predict the future evolution of the PSU's health performance.

pumped storage unit  /  tendency prediction  /  health performance index  /  convolutional neural network  /  long and short memory neural network
Ya-hui SHAN, Hao WANG, Gen-ping WU, Jie LIU. A Health Performance Tendency Prediction Model of Pumped Storage Unit Based on Convolution Neural Network-long Short-term Memory Neural Network[J]. Water Resources and Power, 2023 , 41 (8) : 185 -187 . DOI: 10.20040/j.cnki.1000-7709.2023.20230182
Appendix
Less
Year 2023 volume 41 Issue 8
PDF
122
18
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20230182
  • Receive Date：2023-02-10
  • Online Date：2026-01-28
  • Published：2023-08-25
Article Data
Affiliations
History
  • Received：2023-02-10
  • Revised：2023-04-04
Funding
Affiliations
    1.Wuhan Second Ship Design and Research Institute, Wuhan 430064, China
    2.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20230182
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House