学术文献库

Active yaw control (AYC) of wind turbines has been widely applied to increase the annual energy production (AEP) of a wind farm. AYC efficiency depends on the wind direction and the wind farm layout because an AYC method utilizes wake deflection by yawing wind turbines. Conventional optimization of a wind farm layout assumed that the swept areas of all wind turbines are aligned perpendicular to the wind direction, thereby allowing non-optimal utilization of an AYC method. Higher AEP can be obtained by joint optimization which considers an AYC method in the layout design stage. Joint optimization of the farm layout and AYC has been difficult due to the non-convexity of the problem and the computational inefficiency. In the present study, a particle swarm optimization based method is developed for joint optimization. The layout is optimized with simultaneous consideration for yaw angles for all wind velocities to obtain a globally optimal layout. A number of random initial particles consisting of the layout and yaw angles of wind turbines reduce the initial layout dependency on the optimized layout. To deal with the challenge of large-scale optimization, the adaptive granularity learning distributed particle swarm optimization algorithm is implemented. The improvement in AEP when using a jointly optimized layout compared to a conventionally optimized layout in a real wind farm is demonstrated using the present method.