水轮机特性曲面型值点延拓神经网络仿真研究

Research on Hydro-turbine Characteristic Curve Surface Characteristic-points Extension Based on Neural Networks

为获得小流量、小开度区域水轮机调节特性数据,采用遗传算法优化BP网络神经元连接参数,同时联合比例间隔型值点延拓算法,对水轮机的力矩和流量特性相对值数据进行学习训练和全工况区延拓仿真。实验结果表明,将水轮机多值非线性复杂调节特性转换为空间特性曲面,经神经网络系统自动学习训练,实现综合特性曲线的数值化、智能化延拓处理。建立的水轮机非线性神经网络模型,能预测生成全工况准确数据,为机组调节保证计算及控制决策提供详细数据支撑。

In order to obtain the turbine adjustment characteristics data in the small flow and small opening areas,based on genetic algorithm to optimize the connection parameters of BP neural simulation networks and proportional interval characteristic-points fitting algorithm to extend the curve surface,the turbine torque and flow characteristics relative value data has been trained and extended to all operation areas.The experimental results show that the turbine integrated characteristic curve has been numerically and intelligently extended processing by hydro-turbine＇s complex adjustment characteristics. The accurate data in all operation areas generated by the nonlinear neural network model of hydraulic turbine built in this paper can provide a detailed data support basis to ensure the control decisions for the regulation guarantee calculation of hydro-turbine.