Complete characteristic curve processing method based on improved backpropagation neural network and Logarithmic curve projection

Abstract

The calculation of transient process is the basis of the design and construction of pumped storage power plant, which directly affects operation stability of pumped storage units. However, for satisfying the design of uniform and smooth flow in both directions, the complete characteristic curves of pumped turbine show a significant S feature, which brings difficulties to the interpolation calculation in the transient process as there are crossover and overlap phenomenon in pump and turbine working conditions. In this paper, a transformation method for complete characteristic curves based on logarithmic curve projection and improved backpropagation neural network is presented to solve the above problem. Furthermore, the feasibility and accuracy of the method are verified through the comparison of load rejection condition with on-site measurement, the results show that the proposed method overcomes the multi-value problem that exists in the original curve, in especial makes the small opening region well expressed. The simulation based on the transformed curve reaches a high similarity of transient process with the field test, both in the trend and extreme values, which provides great convenience for the calculation of transient process.