The Vibration Analysis And Dynamic Identification Of Hydropower House Structure

With the rapid development of hydropower construction in our country, the capacity and dimension of hydroelectric generating unit increase constantly, and the rotational speed increases correspondingly. The vibrating problem of water turbine generator set emerging, which becomes an important problem to be studied. The dynamic load on hydroelectric generating units when operating is the basic data of dynamic design and vibration analysis. However, because the generator set is so large-sized and the load distribution is very complicated, it is difficult to measure dynamic load directly. So, it is significant that using load identification technique to identify dynamic load. Compared with other traditional methods, Neural networks has an unparalleled advantage and applied to more and more recognition of engineering structures after years of development. To master the vibration characteristic of hydropower station structure well and resolve the vibration problem, the best method is the field test. Then start a feedback analysis to the experimental data. At the same time, the finite elements method is used to calculate the dynamic response of the power house. In this paper, the author tries to identity dynamic load of generator set by neural network. This paper's primary studies and results such as:(1) Applying improved BP network into a numeric example to identify different dynamic load, based on the good effect, the dynamic load on generator pier and hydro turbine shaft system has been identified and achieved good result.(2) The vibration rule was analyzed according to the co-vibration test data of generating unit and hydropower house structures. The finite elements method is used to calculate the dynamic response of the power house. The vibration of the generator set and power house is evaluated according to the corresponding rules.(3) The obviously coupling effect and correlation between the vibration of generating unit and hydropower house structures was verified. Thus, Neural network model is established so as to predict vibration acceleration of hydropower house structures. The validity of the predicted result is verified.