A Hybrid Feature Selection and Construction Method for Detection of Wind Turbine Generator Heating Faults

Data preprocessing is crucial to create effective machine learning applications. Feature construction and selection are powerful techniques used for this aim. In this paper, a feature selection and construction approach is presented for the detection of wind turbine generator heating faults. The data used for this study was obtained from the Supervisory Control and Data Acquisition (SCADA) system of a wind turbine. The original features directly collected from the data collection system consist of wind characteristics, operational data, temperature measurements and status information. In addition to these original features, new features were created in the feature construction step to obtain information that can be more powerful indications of the faults. After the construction of new features, a hybrid feature selection technique was implemented to find out the most relevant features in the overall set to increase the classification accuracy and decrease the computational burden. Feature selection involves two parts which are filter-based and wrapper-based approaches. Filter based feature selection was applied to exclude the features which are non-discriminative and wrapper-based method was used to determine the final features considering the redundancies and mutual relations amongst them. Artificial Neural Networks were used both in the detection phase and as the induction algorithm of the wrapper-based feature selection part. The results show that, the proposed approach contributes to the fault detection system to be more reliable especially in terms of reducing the number of false fault alarms.