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

doi:10.1088/1674-1056/ac04a9

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.

Keywords: hydro-turbine governing system stability machine learning dynamic model

Received: 06 April 2021
Revised: 13 May 2021
Accepted manuscript online: 25 May 2021

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	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)

Corresponding Authors: Yuansheng Chen
E-mail: chenyuanshengants@163.com

Cite this article:

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

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