Stability analysis of hydro-turbine governing system based on machine learning

doi:10.1088/1674-1056/ac04a9

中国物理B ›› 2021, Vol. 30 ›› Issue (12): 120509-120509.doi: 10.1088/1674-1056/ac04a9

Stability analysis of hydro-turbine governing system based on machine learning

Yuansheng Chen(陈元盛)^† and Fei Tong(仝飞)

State Key Laboratory of Eco-hydraulic in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China

收稿日期:2021-04-06 修回日期:2021-05-13 接受日期:2021-05-25 出版日期:2021-11-15 发布日期:2021-12-01
通讯作者: Yuansheng Chen E-mail:chenyuanshengants@163.com

Stability analysis of hydro-turbine governing system based on machine learning

Yuansheng Chen(陈元盛)^† and Fei Tong(仝飞)

State Key Laboratory of Eco-hydraulic in Northwest Arid Region, Xi'an University of Technology, Xi'an 710048, China

Received:2021-04-06 Revised:2021-05-13 Accepted:2021-05-25 Online:2021-11-15 Published:2021-12-01
Contact: Yuansheng Chen E-mail:chenyuanshengants@163.com

摘要/Abstract

摘要： Hydro-turbine governing system is a time-varying complex system with strong non-linearity, and its dynamic characteristics are jointly affected by hydraulic, mechanical, electrical, and other factors. Aiming at the stability of the hydro-turbine governing system, this paper first builds a dynamic model of the hydro-turbine governing system through mechanism modeling, and introduces the transfer coefficient characteristics under different load conditions to obtain the stability category of the system. BP neural network is used to perform the machine study and the predictive analysis of the stability of the system under different working conditions is carried out by using the additional momentum method to optimize the algorithm. The test set results show that the method can accurately distinguish the stability category of the hydro-turbine governing system (HTGS), and the research results can provide a theoretical reference for the operation and management of smart hydropower stations in the future.

关键词: hydro-turbine governing system, stability, machine learning, dynamic model

Abstract: Hydro-turbine governing system is a time-varying complex system with strong non-linearity, and its dynamic characteristics are jointly affected by hydraulic, mechanical, electrical, and other factors. Aiming at the stability of the hydro-turbine governing system, this paper first builds a dynamic model of the hydro-turbine governing system through mechanism modeling, and introduces the transfer coefficient characteristics under different load conditions to obtain the stability category of the system. BP neural network is used to perform the machine study and the predictive analysis of the stability of the system under different working conditions is carried out by using the additional momentum method to optimize the algorithm. The test set results show that the method can accurately distinguish the stability category of the hydro-turbine governing system (HTGS), and the research results can provide a theoretical reference for the operation and management of smart hydropower stations in the future.

Key words: hydro-turbine governing system, stability, machine learning, dynamic model

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

05.90.+m (Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems) 42.65.Sf (Dynamics of nonlinear optical systems; optical instabilities, optical chaos and complexity, and optical spatio-temporal dynamics) 45.20.dh (Energy conservation)

Yuansheng Chen(陈元盛) and Fei Tong(仝飞). Stability analysis of hydro-turbine governing system based on machine learning[J]. 中国物理B, 2021, 30(12): 120509-120509.

Yuansheng Chen(陈元盛) and Fei Tong(仝飞). Stability analysis of hydro-turbine governing system based on machine learning[J]. Chin. Phys. B, 2021, 30(12): 120509-120509.

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Stability analysis of hydro-turbine governing system based on machine learning

Stability analysis of hydro-turbine governing system based on machine learning

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