Data-driven condition monitoring approaches to improving power output of wind turbines

This paper presents data-driven approaches to improving active power output of wind turbines based on estimating their health condition. The main procedure includes estimations of fault degree and health condition level, and optimal power dispatch control. The proposed method can adjust active power output of individual turbines according to their health condition and can thus optimize the total energy output of wind farm. In the paper, extreme learning machine (ELM) algorithm and bonferroni interval are applied to estimate fault degree while analytic hierarchy process (AHP) is used to estimate the health condition level. A scheme for power dispatch control is formulated based on the estimated health condition. Models have been identified from supervisory control and data acquisition (SCADA) data acquired from an operational wind farm, which contains temperature data of gearbox bearing and generator winding. The results show that the proposed method can maximize the operation efficiency of the wind farm while significantly reduce the fatigue loading on the faulty wind turbines