基于Gasa-BPNN的水轮机系统辨识与预测控制

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.

Hydraulic turbine system identification and predictive control based on GASA-BPNN

Based on the characteristics of nonlinearity, multi-case, and multi-disturbance, it is difficult to establish an accurate parameter mod-el on the hydraulic turbine system which is limited by the degree of fitting between parametric model and actual model, and the design of con-trol 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 control-ler 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.