收藏切换
On-line estimation of primary frequency regulation capability of deep peak regulation thermal power unit based on LSTM neural network
收藏切换
PDF
Xiaoke ZHANG1, Zijie WANG2, Dawei XIA1, Jianbo WANG3, Huaizhong HU2
Thermal Power Generation | 2023, 52(8) : 172 - 178
Less
收藏切换
Thermal Power Generation | 2023, 52(8): 172-178
Power generation technology forum
On-line estimation of primary frequency regulation capability of deep peak regulation thermal power unit based on LSTM neural network
Full
Xiaoke ZHANG1, Zijie WANG2, Dawei XIA1, Jianbo WANG3, Huaizhong HU2
Affiliations
  • 1.Electric Power Research Institute of Stata Grid Henan Electric Power Company, Zhengzhou 450052, China
  • 2.School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710100, China
  • 3.Stata Grid Henan Electric Power Company, Zhengzhou 450000, China
Published: 2023-08-25 doi: 10.19666/j.rlfd.202212211
Outline
收藏切换

With the promotion of China's "carbon peaking and carbon neutral" strategy, thermal power units are more involved in deep peak regulation. Under the conditions of deep peak regulation, the thermal power unit is insufficient in heat storage, and the primary frequency regulation capability decreases, resulting in a large deviation between the unit's primary frequency regulation capability calibrated under the rated operating condition and the actual frequency regulation capability, threatening the frequency security of the power grid. Aiming at this problem, an online estimation method of primary frequency regulation capability of deep peak regulation thermal power units based on LSTM neural network is proposed. The static model of steady-state unit design was improved to a dynamic model, considering the dynamic operation process of the unit by using the time sequence memory ability and nonlinear feature extraction ability of LSTM neural network, and the errors caused by the disturbance factors such as the load changing process and the historical action of primary frequency regulation were corrected. Based on the hierarchical modeling method, the sub-models with different neural network structures were designed for the different characteristics of the factors affecting the frequency regulation capacity, such as heat storage of the unit and steam turbine work performance, and the effects of furnace side were taken into account to improve the accuracy of frequency regulation estimation results. Compared with the traditional method used in the power system, the estimation result of this method has higher accuracy, and has better performance under different working conditions such as steady state and variable load.

deep peak regulation  /  primary frequency regulation  /  LSTM neural network  /  online estimation
Xiaoke ZHANG, Zijie WANG, Dawei XIA, Jianbo WANG, Huaizhong HU. On-line estimation of primary frequency regulation capability of deep peak regulation thermal power unit based on LSTM neural network[J]. Thermal Power Generation, 2023 , 52 (8) : 172 -178 . DOI: 10.19666/j.rlfd.202212211
Year 2023 volume 52 Issue 8
PDF
58
7
Cite this Article
BibTeX
Article Info
doi: 10.19666/j.rlfd.202212211
  • Online Date:2026-01-26
  • Published:2023-08-25
Article Data
Affiliations
History
  • Revised:2022-12-26
Affiliations
    1.Electric Power Research Institute of Stata Grid Henan Electric Power Company, Zhengzhou 450052, China
    2.School of Automation Science and Engineering, Xi'an Jiaotong University, Xi'an 710100, China
    3.Stata Grid Henan Electric Power Company, Zhengzhou 450000, China
References
Share
https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202212211
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

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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