Based on the characteristics of nonlinearity, multi-case, and multi-disturbance, it is difficult to establish an accurate parameter model on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model, and the design of control algorithm has a certain degree of limitation. Aiming at the modeling and control problems of hydraulic turbine system, this paper proposes hydraulic turbine system identification and predictive control based on genetic algorithm-simulate anneal and back propagation neural network (GASA-BPNN), and the output value predicted by GASA-BPNN model is fed back to the nonlinear optimizer to output the control quantity. The results show that the output speed of the traditional control system increases greatly and the speed of regulation is slow, while the speed of GASA-BPNN predictive control system increases little and the regulation speed is obviously faster than that of the traditional control system. Compared with the output response of the traditional control of the hydraulic turbine governing system, the neural network predictive controller used in this paper has better effect and stronger robustness, solves the problem of poor generalization ability and identification accuracy of the turbine system under variable conditions, and achieves better control effect.

基于非线性、多工况、多扰动的特点，受参数模型与实际模型拟合程度的限制，以及控制设计的限制，难以建立准确的水轮机系统参数模型。算法有一定的局限性。针对水轮机系统的建模与控制问题，提出了基于遗传算法-模拟退火和反向传播神经网络（GASA-BPNN），通过GASA-BPNN模型预测的输出值的水轮机系统辨识与预测控制。反馈给非线性优化器输出控制量。结果表明，传统控制系统的输出速度大幅度提高，调节速度慢，而 GASA-BPNN 预测控制系统的速度增加很小，调节速度明显快于传统控制系统。与水轮机调节系统传统控制的输出响应相比，本文采用的神经网络预测控制器具有更好的效果和更强的鲁棒性，解决了水轮机系统在变工况下泛化能力和辨识精度差的问题，并达到更好的控制效果。