| In view of the growing demand for energy,it is essential to integrate renewable energy into the electricity supply.As a renewable energy with the characteristics of green,clean,environmental protection and high economic benefits,wind energy is of great significance to the sustainable development of human society.In recent years,offshore wind power generation has accounted for an increasing proportion of the power system,but wind turbines do not contribute to the inertia of the power system,which brings considerable challenges to the grid stability of the power system.In this context,a virtual inertia Control method based on Model Predictive Control is proposed to suppress the frequency fluctuation and improve the frequency stability of doubly fed wind power system.The main research content of this thesis includes the following aspects:First of all,this thesis starts from the generator side converter and takes the doubly fed induction generation as the research object to analyze its operating principle and mathematical model.Based on this,the DFIG grid-connected model is built in the Matlab/Simulink simulation platform,and the frequency modulation capability of the traditional virtual inertia control method is analyzed through simulation.Secondly,in order to solve the problem that traditional virtual inertia control method can not provide effective inertia support according to the actual situation of doubly fed wind power system,a MPC virtual inertia control method based on step response modeling is studied,which uses the step response of the system to establish an approximate linear prediction model and improves the system frequency through online rolling optimization.The control parameters are optimized by improved pigeon swarm algorithm.Compared with similar methods through simulation experiments,the results show that the MPC virtual inertia control method based on step response modeling can more effectively suppress frequency fluctuations and improve system stability.Then,considering the strong nonlinearity of the doubly fed wind power system and the control performance of MPC is closely related to the accuracy of the prediction model,it is necessary to strengthen the modeling accuracy in order to further enhance the frequency fluctuation suppression effect.Therefore,deep learning is introduced into model predictive control,and a MPC virtual inertia control method based on recurrent neural network modeling with attention mechanism is studied.A nonlinear prediction model is established by using an encoder-decoder network with attention mechanism,and the improved pigeon swarm algorithm is used to solve the compensation power in the optimization process,which reduced the amount of calculation.Compared with similar methods through simulation experiments,the results show that the MPC virtual inertia control method based on attention mechanism can suppress frequency fluctuations more efficiently and ensure the stability of the power grid.Finally,a virtual inertia control method based on multi-objective MPC is studied,which can effectively solve the problem of complex and difficult to implement the calculation process of hardware modulator in virtual inertia control system.The current-frequency multi-objective optimal control of doubly fed wind turbine is designed based on finite set model predictive control.The frequency response model adopts the trained recurrent neural network model.The current prediction model is obtained by discretization of the current differential equation.In addition,the improved pigeon swarm algorithm is used to dynamically adjust the weight coefficient of the target to adjust the support capacity for the frequency of the system,and better coordinate the quality requirements for different output volumes in real situations.Compared with similar methods through simulation experiments,the results show that the multi-objective MPC virtual inertia control method not only saves resources,but also significantly suppresses frequency fluctuations. |
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