This paper presents the classification of power quality (PQ) disturbances using modular probabilistic neural network (MPNN), support vector machines (SVMs) and least square support vector machines (LS-SVMs) in grid-connected wind energy systems. Different types of sag and swell disturbances due to the change in load and wind speed are created using MATLAB/Simulink. Classification scheme encompasses suitable features extracted by S-transform (ST) and is subsequently trained with MPNN, SVM and LS-SVM to effectively classify the PQ disturbances. The accuracy and reliability of the proposed classifier are also validated on signals with noise content. A comparative study is also carried out to determine the efficacy of the proposed techniques.
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