Identification Of Blade Vibration Causes In Wind Turbine Generators

Abstract

For many years Data Mining techniques have been used to identify patterns and hidden information in datasets from a variety of domains. This paper will outline the initial application of DM techniques on a dataset from the Renewable Energy domain. For many years company's which operate wind farm sites have recorded and archived a range of data from numerous sources across wind farm sites and the wind turbine generators (WTG's) which make up these sites. The study presented in this paper encompasses a first phase of Knowledge Discovery from the dataset, making use of the complementary techniques Neural Networks (NN) and Rule Induction (RI). We discuss the challenges presented by the dataset in terms of selection and preparation of data and also introduce the format and meaning of data encompassed by the area of study, namely effects of blade vibration. Investigation of blade vibration has been used as an initial test of KD within the dataset as we are aware that blade vibration typically occurs within a specific range of wind speeds. By applying the NN and RI techniques we are able to support the theory that vibration occurs within a given range but we have also identified that other variables, namely rotor speed can contribute to blade vibration at lower than expected speeds. l Introduction Knowledge Discovery has enjoyed significant success in the widest possible range of domains during the last decade, from telecommunications to supermarkets to medicine. Various terms have been used to describe the task of finding knowledge from dataset, including Data Mining and Knowledge Discovery. However, it is now generally accepted that all of these terms can be grouped under the more comprehensive title of KDD, Knowledge Discovery in