Recognition and assessment of different factors which affect flicker in wind turbines

Abstract

Similar to other distributed generation sources, wind turbines cause power quality disturbances (PQDs) issues in power systems. One of the most important PQDs that has bad effects on sensitive loads is flicker. In this study, an algorithm is presented for assessment and recognition of different factors causing flicker of wind turbines. Some aerodynamic factors (wind shear and tower shadow) and some mechanical factors (blade pitching errors, gearbox tooth crash and turbine blade break down) are modelled using fixed speed wind turbines. Then, wavelet transform and S-transform are used to extract some dominant features voltage. Then, in order to avoid large dimension of feature vector, Relieff feature selection method is applied to extracted features. The probabilistic neural network (PNN) is used to classify above-mentioned factors. The only adjusted parameter of the PNN classifier is determined by using the particle swarm optimisation technique. Moreover, the short-term severity of flicker (Pst) is calculated for each type of fault as extra features to increase the severability of extracted features. Results show that the classifier can detect different causes of flicker event with high detection accuracy.