Research On Reactive Power Optimization And Coordinated Control Technology Of Large-scale Wind Farm

In recent years,with the large-scale construction of wind farms,the installed capacity of wind power has increased year by year.In the operation of wind farms,on the one hand,there are problems of low power factor at the grid connection point and high line loss;on the other hand,under the violent fluctuation of wind speed,the reactive capacity of wind turbines cannot be fully utilized.In view of the above existing problems,this thesis adopts an improved fuzzy C-means(FCM)aggregation method to control the wind turbines in partitions,and then uses the improved particle swarm optimization algorithm to optimize the reactive power of large-scale wind farms,fully Utilize the reactive power output capability of the wind turbine to reduce the compensation capacity of the static var generator(SVG),and achieve the goal of reducing the line loss of the wind farm and the loss of the SVG itself.The main research content of this thesis is as follows:Firstly,the basic working principle of Doubly-fed Induction Generator(DFIG)is expounded,the three constraints limiting the output reactive power of DFIG stator side are analyzed,the theoretical range of reactive power output on the stator side and rotor side is derived through formulas,and the reactive power limit value of stator side and grid converter output is obtained by changing the state of wind speed in the simulation system and setting the three-phase short circuit fault point.The simulation results verify the correctness of the theoretical analysis.Secondly,the reactive power optimization control algorithm when the output active power of wind turbines in large-scale wind farms is controlled by Maximum Power Point Tracking(MPPT)is studied.The particle swarm algorithm is improved,and the improved particle swarm algorithm is applied to the reactive power optimization of large-scale wind farms by optimizing the inertia weights and learning factor parameters,and combining with the immune algorithm.The application effects of improved particle swarm algorithm,traditional particle swarm algorithm and genetic algorithm in optimizing reactive power allocation in large-scale wind farms are comparatively analyzed.The results show that the improved particle swarm algorithm proposed in this thesis can make the adaptation value of the combination of wind farm network loss,SVG loss and wind turbine output active power smaller,that is,the total active power output of the system is larger.Finally,in view of the problems of complex control,unfavorable management and uncoordinated control caused by the large number of wind turbines in large-scale wind farms,the improved FCM method is used to partition the wind farm,and the improved particle swarm algorithm is combined to optimize the reactive power of the wind farm,and SVG is coordinated for reactive power compensation control.This control method can reduce the reactive power output of SVG,which has the effect of reducing the loss of system reactive power compensation equipment,and at the same time can make the adaptation value of large-scale wind farms lower.On this basis,a wind farm reactive power optimization management platform is designed,which can monitor and regulate the reactive power output of wind turbines and SVG in wind farms in real time,and improve the overall regulation level of the system.