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Abstract This paper describes a set of anomaly‐detection techniques and their applicability to wind turbine fault identification. It explains how the anomaly‐detection techniques have been adapted to analyse supervisory control and data acquisition data acquired from a wind farm, automating and simplifying the operators' analysis task by interpreting the volume of data available. The techniques are brought together into one system to collate their output and provide a single decision support environment for an operator. The framework used is a novel multi‐agent system architecture that offers the opportunity to corroborate the output of the various interpretation techniques in order to improve the accuracy of fault detection. The results presented demonstrate that the interpretation techniques can provide performance assessment and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines. Copyright © 2009 John Wiley & Sons, Ltd.
Zaher et al. (Tue,) studied this question.