Self-Optimizing Control System to Maximize Power Extraction and Minimize Loads on the Blades of a Wind Turbine

This research proposes a methodology for designing and testing a self-optimizing control (SOC) algorithm applied to a wind energy conversion system (WECS). The SOC maximizes WECS power output and reduces the mechanical stress of the wind turbine (WT) blades by optimizing a multiobjective cost function. The cost function computation uses a combined blade element momentum (BEM) and thin-wall beam (TWB) model for calculating wind the turbine power output and blades' stress. The SOC deployment implies a low computational cost due to an optimization space reduction via a matrix projection applied to a measurement vector, based on a prior offline calculation of a projection matrix, H. Furthermore, the SOC optimizes the operation of the WECS in the presence of uncertainty associated with the wind speed variation by controlling a linear combination of measured variables to a set point. A MATLAB simulation of a wind turbine model allows us to compare the WECS operating with the SOC, a baseline classic control system (BCS), and a nonlinear model predictive controller (NMPC). The SOC algorithm is evaluated in terms of power output, blades' stress, and computational cost against the BCS and NMPC. The power output and blades' stress performance of the SOC algorithm are compared with that of the BCS and NMPC, showing a significant improvement in both cases. The simulation results demonstrate that the proposed SOC can effectively optimize a WECS operation in real time with minimal computational costs.