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

Abstract

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.

Keywords

hydraulic turbine system / system identification / genetic algorithm / simulated annealing algorithm / predictive control

References

[1] Working Group Prime MoverEnergy Supply. Hydraulic turbine and turbine control models for system dynamic studies. IEEE Trans. Power Syst., 1992, 7(1): 167.

[2] C.J. Cao and Y.P. Mo, Simulation research on nonlinear controller of hydoeletric generating unit based on the theory of neural network inverse system, China Rural Water Hydropower, 2009, No. 7, p. 122.

[3] J.S. Chang and J. Ren, Analytical theory and method of 3D cavitation flow in hydraulic turbine runner of three gorges power station, J. China Agric. Univ., 1998, No. 2, p. 93.

[4] Johansen TA, Shorten R, Murray-Smith R. On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models. IEEE Trans. Fuzzy Syst., 2000, 8(3): 297.

[5] Chen ZH. Study on Parameter Identification and Control Laws of Hydraulic Turbine Regulating System, 2017, Wuhan, Huazhong University of Science and Technology

[6] L.H. Peng, Y. Song, D. Liu, S.L. Meng, and Z.H. Xiao, Research on nonlinear modeling method of hydraulic turbine based on GA-BP neural network, China Rural Water Hydropower, 2017, No. 4, p. 184.

[7] Tang F. Parameter identification of hydro-generator model based on global information fusion particle swarm optimi-zation. Water Res. Power, 2015, 33(8): 129

[8] Chen ZH, Yuan XH, Tian H, Ji B. Improved gravitational search algorithm for parameter identification of water turbine regulation system. Energy Convers. Manage., 2014, 78, 306.

[9] Wan RW, Zhou QQ. Hyrdaulic turbine regulating system based on adaptive fuzzy intelligent PID controller. Yangtze River, 2016, 47(4): 79

[10] An JT. Research and Design of Intelligent PID Controller Based on Fuzzy Neural Network, 2010, Wuhan, Wuhan University of Technology

[11] Liu JJ. The Simulation Study of Hydropowe Governing System Based On Fuzz-PID, 2012, Xianyang, Northwest Agriculture and Forestry University

[12] Li CS, Zhou JZ. Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm. Energy Convers. Manage., 2011, 52(1): 374.

[13] Wang SQ, Zeng SQ, Wu S, Pan J, Zhuang XK, Yuan XH. Prediction control of hydroelectric generator based on NNARX dynamic model. Water Res. Power., 2014, 32(3): 192

[14] Liu X. Generalized Predictive Control of Hydraulic Turbine Governing System Based T-S Fuzzy Model, 2016, Wuhan, Huazhong University of Science and Technology

[15] Z.H. Xiao, S.Q. Wang, H.T. Zeng, and X.H. Yuan, Identifying of hydraulic turbine generating unit model based on neural network, [in] Sixth International Conference on Intelligent Systems Design and Applications, Jinan, 2006, p. 113.

[16] Xi YG, Chai TY, Yun WM. Survey on genetic algorithms. Control Theory Appl., 1996, 6, 697

[17] Li ZT, Wang SL, Zhang GL. Application genetic and simulated annealing algorithm for optimization of neural network structure. Comput. Eng. Appl., 2007, 43(36): 74

[18] Chai YM. Optimizing BP Neural Network Using Improved Simulated Annealing Genetic Algorithm, 2008, Changchun, Jilin University

[19] Qian JX, Zhao J, Xu ZH. Predictive Control, 2007, Beijing, Chemical Industry Press

[20] Zheng Y. Research on Model Predictive Control of Hydropower Unit Regulating System, 2018, Wuhan, Huazhong University of Science and Technology