Home Journals Articles Updates About
CN
image
收藏切换
Condition Assessment of Peaking Power Source Based on Data Fusion and Parallel Feature Extraction
收藏切换
PDF
Feng HAO1, Bing FANG2, Wei-wen QI2, Qin-hui GUO2, Chuan-gu ZHU3, Wei-feng PAN3
Water Resources and Power | 2023, 41(5) : 203 - 206
Less
收藏切换
Water Resources and Power | 2023, 41(5): 203-206
• ELECTRICAL ENGINEERING •
Condition Assessment of Peaking Power Source Based on Data Fusion and Parallel Feature Extraction
Full
Feng HAO1, Bing FANG2, Wei-wen QI2, Qin-hui GUO2, Chuan-gu ZHU3, Wei-feng PAN3
Affiliations
  • 1.Water and Innovation Department, State Grid Corporation of China, Beijing 100031, China
  • 2.Shaoxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Shaoxing 310000, China
  • 3.State Grid Electric Power Research Institute, Nanjing 211106, China
Published: 2023-05-25 doi: 10.20040/j.cnki.1000-7709.2023.20221390
Outline
收藏切换

For the task of condition evaluation of peaking power equipment, the monitoring indicators are defined as a set of time series, among which the complex coupling relationships need to be considered. Besides, requirements on processing time are introduced by the real-time systems. A new framework is proposed to address these problems via the technology of parallel feature extraction as well as data fusion. The time series are statistically analyzed in parallel where multiple hypothesis testing is used to select the important features. Using a defined hierarchical graph convolutional network, the related information is integrated for the final condition evaluation task. Compared with the existing models, experiments indicate that the proposed method with stronger transferability and shorter processing time has a much higher predication accuracy.

condition assessment of peaking power equipment  /  parallel time series data processing  /  graph neural network  /  feature extraction  /  data fusion
Feng HAO, Bing FANG, Wei-wen QI, Qin-hui GUO, Chuan-gu ZHU, Wei-feng PAN. Condition Assessment of Peaking Power Source Based on Data Fusion and Parallel Feature Extraction[J]. Water Resources and Power, 2023 , 41 (5) : 203 -206 . DOI: 10.20040/j.cnki.1000-7709.2023.20221390
Appendix
Less
Year 2023 volume 41 Issue 5
PDF
71
11
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20221390
  • Receive Date：2022-07-03
  • Online Date：2026-01-28
  • Published：2023-05-25
Article Data
Affiliations
History
  • Received：2022-07-03
  • Revised：2022-10-12
Funding
Affiliations
    1.Water and Innovation Department, State Grid Corporation of China, Beijing 100031, China
    2.Shaoxing Power Supply Company of State Grid Zhejiang Electric Power Co., Ltd., Shaoxing 310000, China
    3.State Grid Electric Power Research Institute, Nanjing 211106, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20221390
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