Home Journals Articles Updates About
CN
image
收藏切换
Research on Full Head Modeling Method of Turbine Based on Real Machine Data
收藏切换
PDF
Wei-jiang CAI1, Ke-cheng-qi DING2, Lin-ning CAO2
Water Resources and Power | 2023, 41(10) : 180 - 184
Less
收藏切换
Water Resources and Power | 2023, 41(10): 180-184
• ELECTROMECHANICS AND CONTROL ENGINEERING •
Research on Full Head Modeling Method of Turbine Based on Real Machine Data
Full
Wei-jiang CAI1, Ke-cheng-qi DING2, Lin-ning CAO2
Affiliations
  • 1.NARI Group Corporation/State Grid Electric Power Research Institute, Nanjing 211106, China
  • 2.College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
Published: 2023-10-25 doi: 10.20040/j.cnki.1000-7709.2023.20222507
Outline
收藏切换

At present, the TV card model of PSD-PBA platform used in power system stability calculation cannot reflect the difference of units under multiple water heads, which is specifically reflected its dynamic response speed and anti-regulation accuracy. Based on the TV card model, an improved scheme is proposed. The water head-opening-power characteristics and power Tw characteristics of the model are obtained by program processing with the early operation data of the hydropower station, and then added to the improved model successively. The results show that the dynamic characteristics of the TV card model do not have the conditions for multi-parameter identification under the power mode primary frequency modulation. Moreover, the accuracy of anti-regulation under different loads is poor. The response characteristics of the improved model accord with the operation law of the real machine and can accommodate the difference of multiple water heads of the unit.

hydraulic turbine model  /  anti-regulation  /  full head  /  multi-parameter identification  /  flow inertia time constant
Wei-jiang CAI, Ke-cheng-qi DING, Lin-ning CAO. Research on Full Head Modeling Method of Turbine Based on Real Machine Data[J]. Water Resources and Power, 2023 , 41 (10) : 180 -184 . DOI: 10.20040/j.cnki.1000-7709.2023.20222507
Appendix
Less
Year 2023 volume 41 Issue 10
PDF
128
29
Cite this Article
BibTeX
Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222507
  • Receive Date：2022-11-30
  • Online Date：2026-01-28
  • Published：2023-10-25
Article Data
Affiliations
History
  • Received：2022-11-30
  • Revised：2023-01-24
Affiliations
    1.NARI Group Corporation/State Grid Electric Power Research Institute, Nanjing 211106, China
    2.College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China
References
Share
https://castjournals.cast.org.cn/joweb/sdnykx/EN/10.20040/j.cnki.1000-7709.2023.20222507
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