An evolving classification approach for fault diagnosis and prognosis of a wind farm

A wind farm is a complex system composed of several wind turbines operating in a non-stationary environment. Each of the wind turbines is subject to sudden and gradual faults due to operational and environmental conditions, aging etc. In order to assure an optimal power production and reduce maintenance costs, these faults have to be detected, isolated as soon as possible, and predicted. In this paper, an evolving classification method is proposed to achieve these requirements. The proposed approach is data-driven and does not require prior physical knowledge, in particular wind dynamics. It is based on the dynamic classification algorithm AUDyC. The considered features are determined according to the difference between generated electric powers regarding several operating modes. Normal operating modes are represented by classes in a decision space. Each new measure is classified on line. Indicators are computed to detect and isolate the occurrence of faults. Finally, a predictive method is implemented to forecast the degradation state of the wind turbine. A wind farm benchmark model, proposed for a fault diagnosis and fault tolerant control competition is used to highlight the efficiency of the proposed approaches.