A synthesis of feasible control methods for floating offshore wind turbine system dynamics. (November 2021) - Shah, Kamran Ali; Meng, Fantai; Li, Ye; Nagamune, Ryozo; Zhou, Yarong; Ren, Zhengru; Jiang, Zhiyu
Abstract: During the past decade, the development of offshore wind energy has transitioned from near shore with shallow water to offshore middle-depth water regions. Consequently, the energy conversion technology has shifted from bottom-fixed wind turbines to floating offshore wind turbines. Floating offshore wind turbines are considered more suitable, but their cost is still very high. One of the main reasons for this is that the system dynamics control method is not well-adapted, thereby affecting the performance and reliability of the wind turbine system. The additional motion of the platform tends to compromise the system's performance in terms of power maximization, power regulation, and load mitigation. To provide a recommendation based on the advantages and disadvantages of different control methods, we systematically analyze feasible control methods for existing floating offshore wind turbine designs. Based on a brief overview of floating offshore wind turbine system dynamics, we present several promising control methods by classifying them as blade-pitch-based and mass–spring–damper-based. Furthermore, we emphasize on the incoming wind and wave forecasting associated with the control methods. We then compare different methods by evaluating a matrix involving platform motion minimization, load mitigation, and power regulation and identify the advantages and disadvantages. Finally, recommendations and suggestions for further research are provided by integrating the advantageous control algorithm and forecasting technologies to reduce costs. Highlights: Various control methods of floating offshore wind turbine systematic dynamics are reviewed. Incoming wind and wave forecasting techniques are presented. Advantages and disadvantages of different methods with respect to platform design is discussed. Incident disturbance-based control method improvement is proposed with machine learning.