Decentralized Individual Pitch Control with Inverted Decoupling for Wind Turbines in the Full Load Region

In order to mitigate periodic blade loads in wind turbines, recent research has analyzed different Individual Pitch Control (IPC) approaches, which typically use the multi-blade coordinate (MBC) transformation. Some of these studies investigate methods to enhance the performance of IPCs, without considering the increased control effort performed by the pitch signal, which is the main negative side effect. Another crucial factor that contributes to the deterioration of IPC performance is the significant coupling observed in the non-rotating axes within the MBC transformation. In this work, both adverse aspects are addressed. A decentralized control with inverted decoupling is proposed as a control structure, with the intention of comparing it to the traditional IPC. The inverted decoupling is designed from an approximated multivariable linear model. The trade-off between pitch signal effort and blade fatigue reduction is addressed for IPC applied to a wind turbine operating in the full load region. Here, two IPC schemes, with and without inverted decoupling, are designed and applied to a 15 MW monopile offshore wind turbine simulated with OpenFAST software. The optimal tuning of the IPC parameters is performed by means of a multi-objective optimization solved by genetic algorithms. The optimization procedure minimizes two objective functions related to pitch signal effort and blade fatigue load. The resulting Pareto fronts show a range of optimal solutions for each IPC scheme. The selected optimal solution for IPC with inverted decoupling compared to the optimal solution for IPC without inverted decoupling achieves improvements of more than 11% in blade load reduction maintaining similar pitch signal effort. The primary innovation of this paper is the application of inverted decoupling in the native IPC structure and the use of GA to address the trade-off between blade load fatigue and pitch signal effort.