Distributed Model Predictive Control of a Wind Farm for Optimal Active Power ControlPart II: Implementation With Clustering-Based Piece-Wise Affine Wind Turbine Model

Abstract

This paper presents distributed model predictive control (D-MPC) of a wind farm for optimal active power control using the fast gradient method via dual decomposition. The objectives of the D-MPC control of the wind farm are power reference tracking from the system operator and wind turbine mechanical load minimization. The optimization of the active power control of the wind farm is distributed to the local wind turbine controllers. The D-MPC developed was implemented using the clustering-based piece-wise affine wind turbine model. With the fast gradient method, the convergence rate of the D-MPC has been significantly improved, which reduces the iteration numbers. Accordingly, the communication burden is reduced. A wind farm with ten wind turbines was used as the test system. Case studies were conducted and analyzed, which include the operation of the wind farm with the D-MPC under low and high wind conditions, and the dynamic performance with a wind turbine out of service. The robustness of the D-MPC to errors and uncertainties was tested by case studies with consideration of the errors of system parameters.