Soumya R. Mohanty, Nand Kishor, Prakash K. Ray, and João P.S. Catalão
Wind energy, Power quality, Classification, Support vector machines
This paper presents the effect of environmental factors, such as wind speed change, on the classification of power quality (PQ) disturbances in grid-connected wind energy systems. Initially, based on the selection of suitable features and 3-Dimensional feature plots, the PQ disturbances are classified. Further, the disturbances are accurately classified using S-transform based feature extraction followed by classification by modular probabilistic neural network (MPNN), support vector machines (SVMs) and least square support vector machines (LS-SVMs). Different types of sag and swell disturbances due to the change in load and wind speed are created using MATLAB/Simulink and an experimental prototype setup for the classification problem. The results reveal that S-transform based extracted feature data, when trained with MPNN, SVMs and LS-SVM, can 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 robustness of the techniques used. Finally, conclusions are duly drawn.
Important Links:
Go Back
