Distributed economic model predictive control of wind farms
With the continuous expansion of the scale of wind farms,the wake effect leads to the problem of reduced power generation and increased fatigue load of downstream wind turbines in the farm,which is becoming increasingly serious.To reduce the operating cost of wind farms and improve dynamic economic performance,this paper proposes a hierarchical control structure for wind farms.At the upper level,the maximum wind energy capture of the entire farm under the current wind direction is achieved by optimizing the induction factor of the wind turbines in the entire farm,providing the optimal load reduction tracking power benchmark for each turbine for the local control of the lower layer.At the lower level,a stable distributed economic model predictive control strategy based on terminal area constraints is used to implement local control of each wind turbine,ensuring that the load demand of the power grid is met while effectively reducing the fatigue load of the turbine and improving the dynamic economy of the wind farm.Finally,a wind farm consisting of nine wind turbines was simulated by SimWindFarm software to verify the effectiveness of the designed control strategy under three conditions:wind direction change,step wind speed disturbance,and turbulent wind speed disturbance.
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