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

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.     

基于频域衰减系数的Al Cu Mg铝合金激光超声评价

本文采用激光超声无损检测技术对Al Cu Mg铝合金进行检测,通过变分模态分解法(Variational Modal Decomposition,VMD)提取了激光超声信号的频域衰减系数,并通过扫面电镜(SEM)、电子背散射衍射(EBSD)、拉伸试验和显微硬度试验等实验方法对Al Cu Mg铝合金的微观组织和力学性能进行检测,将激光超声频域衰减系数和Al Cu Mg铝合金的微观组织和力学性能进行耦合。结果表明:衰减系数与析出相含量、平均晶粒尺寸力学性能均有一定的相关性,激光超声检测方法可以识别Al Cu Mg铝合金微观组织和力学性能变化趋势,这为Al Cu Mg铝合金微观组织和力学性能的非接触、快速无损评价提供了一个新方法。