Research On The Evaluation Method Of Virtual Inertia Level Of Large-scale Wind Farms

In modern power grids,the installed capacity of new energy is increasing,and the construction of new power systems characterized by high proportion of new energy is accelerating.In order to make the unstable and uncontrollable energy such as wind power have certain adjustability,people install virtual inertia control on variable speed wind turbine to improve the inertia response ability of power grid,so that wind power can provide inertia support when the system is disturbed.How to evaluate the inertial response capability of high proportion of new energy power grid has become a key problem to be solved urgently in modern power grid,which has important theoretical and application value for the planning and operation of power grid.In this thesis,the virtual inertia evaluation method of large-scale wind farm is studied : the control system model and simulation model of wind turbine inertia control are established,and the simulation research on the action characteristics of wind farm station to grid inertia support is carried out.A wind farm station fan division method based on improved artificial bee colony optimization fuzzy C-means clustering is proposed.An inertia evaluation method of wind farm station based on Copula function correlation analysis is established for different wind conditions.The specific research contents are as follows :Firstly,the basic principle of inertia control of doubly-fed wind turbine is analyzed,and the measurement method of inertia of single wind turbine is studied.On this basis,the control system model and simulation model of wind turbine inertia control are established,and the simulation research is carried out on the action characteristics of wind farm station on the inertia support of power grid.The variation of grid inertia under different wind power penetration ratios and the influence of low inertia on power system are analyzed and deduced.The research work shows that the wind farm station with virtual inertia control has a good supporting effect on the power system and is affected by factors such as wind speed.Then,considering the change of wind speed at different positions in the wind farm station,the wind turbine classification method for wind power inertia evaluation is studied,and a fuzzy C-means clustering method based on improved artificial bee colony optimization is proposed to realize the effective division of wind turbines in wind farm stations.This method comprehensively considers the variable factors such as wind speed,speed and active power of wind turbines,which can realize effective and accurate clustering of multiple wind turbines in wind farms,and provide support for subsequent evaluation of the available inertia of wind farms.Finally,a wind farm station inertia evaluation method based on Copula function correlation analysis is established.The wind speed correlation analysis based on Copula function is carried out on the wind turbine,and the correlation between the wind speed of the cluster center and the wind speed of the wind tower is obtained.The available inertia of the wind turbine is calculated by considering the limit between the speed and active power output of the doubly-fed induction wind turbine under different working conditions of the doubly-fed induction wind turbine under full wind conditions.The total available inertia of the wind farm is obtained by synthesizing the inertia of different clusters,and the confidence interval of the available inertia of the wind farm is obtained.