Construction of Wind Power Generation System Control and Energy Management System Based on Model Predictive Control

With the development of wind turbine control technology, people’s utilization rate of wind energy has been continuously improved, and the scale of wind farms has also been continuously expanded. MPC (Model Predictive Control) is one of the most widely used feedback control strategies. The purpose of this paper is to study the construction of wind power generation system control and energy management system based on model predictive control. A control method combining distributed control with predictive control is proposed. Distributed control can decompose a large-scale complex system into several subsystems, and each subsystem exchanges information through the network, which reduces the complexity of the problem. Based on Pareto optimal theory, the energy management system of wind power generation is established, and the energy scheduling vector of ESS (energy-storage systems) is solved by two-stage hierarchica optimization method, which overcomes its limitations compared with heuristic algorithm and multi-objective optimization algorithm. The research results show that compared with MPC method, the distributed MPC has a good effect in reducing the fluctuation of generator speed under the condition of average wind speed of $6 \mathrm{~m} / \mathrm{s}$. The energy management system of wind power generation based on Pareto optimization theory can adjust and control the power generation unit and energy storage unit in real time, and optimize the operating cost of the system.