Fault detection and diagnosis for wind turbines using data-driven approach

One of the greatest drawbacks in wind energy generation are the high maintenance costs associated to mechanical faults. In order to reduce these impacts have been integrated fault detection system in wind turbines, known as FDD´s (`Fault detection and Diagnosis System´). The approach to the development of FDD systems presented is known as `Data-Driven´ (FDD-DD) which involves the use of collections of data from a monitoring system for building models of classification/regression. The aim of this paper is to perform a comparative analysis of different techniques: decision trees, bayesian classification, neural networks and support vector machines applied to fault detection systems in wind turbines. The results indicate that support vector machines bi-class gets a fairly high level of accuracy like Bayesian classifiers.