A Hybrid Feature Selection and Construction Method for Detection of Wind
  Turbine Generator Heating Faults
Ayse Gokcen Kavaz Burak Barutcu
Preprocessing of information is an essential step for the effective design of
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. Data were collected from 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 step consists of filter and
wrapper-based parts. 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.