This paper presents methods and results in modelling wind turbine dynamic radar signatures in the near-field. The theoretical analysis begins with the simpler case of modelling wind turbine blades as rectangular plates. The theoretical radar signature for the wind turbine in the near-field is formulated and its main peculiarities are investigated. Subsequently, the complex shape of the blades is considered and the corresponding radar signatures are modelled. Theoretical modelling is confirmed for both cases via experimental testing in laboratory conditions. It is shown that the experimental results are in good accordance with the theoretically predicted signatures.

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This paper explores the possibility in using radar to automatically classify wind turbine faults. As a first step, a number of experiments were conducted in an anechoic chamber with a small wind turbine were different faults were artificially induced. Two basic clustering methods were used. One was based on using different statistical parameters of the corresponding time-domain signatures. The other used Principal Components Analysis (PCA) on the corresponding frequency-domain signatures. Subsequently, a K-NN algorithm was used as the classifier to investigate whether or not automatic classification is fundamentally possible and to provide an initial comparison between the two clustering methods which rely on different signal domains. The proof of concept results presented in the paper indicate that this may indeed be plausible, to encourage further development of this idea.