A study of potential approaches to simulate power output as well as identifying anomalous operation of wind turbines

Sammanfattning: From an economical perspective, ice accretion on wind turbines located in cold climates can cause severe and costly production losses. To reduce the cost caused by such factors, it is important to early detect anomalous operation. This requires the knowledge of expected operation for all possible states of operation. The purpose of this M.Sc. thesis was first of all to investigate the feasibility to define a model able to simulate expected power output regardless time of the year. A second purpose was to investigate possible approaches for the identification of wind turbines deviating from expected operation. Regarding the first purpose, two different models were developed to investigate the possibility to simulate expected power output. A deterministic model based on the characteristic power curve and a non-deterministic regression tree model based on machine learning algorithms. As regards the second model, two control charts were implemented and their ability to identify abnormal operation was evaluated. The development and evaluation of the models as well as the control charts were performed in Matlab R2013b.