Open AccessResearch and Analysis of On-Line Optimization Algorithm of Nonlinear Model Predictive Control Based on Hydropower Installation Field
Author(s) -
Xiaoping Gou,
Wanjun Zhang,
Feng Zhang,
Jingxuan Zhang,
Jingyi Zhang,
Jingyan Zhang
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/632/3/032006
Subject(s) - model predictive control , nonlinear system , control theory (sociology) , matlab , nonlinear programming , state space , stability (learning theory) , field (mathematics) , computer science , generator (circuit theory) , dimension (graph theory) , control engineering , engineering , control (management) , mathematics , artificial intelligence , machine learning , power (physics) , statistics , physics , quantum mechanics , pure mathematics , operating system
Because of the strong nonlinearity of the controlled system, it cannot be expressed in the form of state space, and the calculation of nonlinear programming is large, so it is difficult to get the analytical solution. At present, it is generally solved by numerical method. The GPC stability theory based on state space analysis and internal model control is difficult to be applied in this case. In this paper, a fast algorithm of online optimization of nonlinear model predictive control based on the field of hydropower installation is presented. A fast algorithm control system of online optimization of nonlinear model predictive control is established and simulated by MATLAB. The simulation results show that the dimension of nonlinear programming solution in rolling optimization is effectively reduced and the feasibility of MPC is improved. Compared with single machine and multi machine systems, the model predictive control method is proved to have strong ability of generator terminal voltage maintenance and electromechanical oscillation damping.