Cluster-Based Predictive PCC Voltage Control of Large-Scale Offshore Wind Farm

A large number of wind turbine generators in the large-scale offshore wind farm system poses a challenge to the wind farm control system due to the computational burden in the central control methods or the complexity of the communication network in the decentralized control strategies. A hybrid control method based on the distributed consensus control and the central model predictive control is proposed in this study to overcome the problem. Typically, the wind turbine generators in the large-scale offshore wind farm system are clustered into several groups. The consensus-based distributed reactive power coordination control is proposed to each cluster and the centralized predictive voltage control is used to manage the total reactive powers of all clusters and regulate the voltage at the point of common coupling. The gradient-descent algorithm for the optimal design of the consensus-based cluster control is presented firstly. Based on the convergence property of the consensus control, the equivalent model of the total reactive power response of each cluster is identified, which is used for the design of the centralized predictive voltage control. Eigenvalue analysis of the proposed predictive control strategy is carried out to verify the stability of the distributed and predictive control systems. The robustness of the proposed predictive controller is evaluated in the conditions of significant model errors due to the communication delay in each cluster. A comparison study with the full distributed control based on consensus algorithm is presented to demonstrate the effectiveness of the proposed control method. The feasibility of the proposed predictive controller is evaluated by the control-hardware-in-the-loop simulation using OPAL-RT Technologies. An additional comparison study in term of computation time with the central control method is also carried out. Real-time simulation results show the superior performance of the proposed hybrid method compared to the full distributed consensus controller or the central control strategies.