Imbalance classification in a scaled-down wind turbine using radial basis function kernel and support vector machines

This work innovates by proposing the combination of DFA with the SVM and RBFK methods, two supervised algorithms that use the kernel-method, for the imbalance level classification in a scaled-down wind turbine. The results obtained were compared with other techniques proposed in previous works. The vibration signals analyzed were acquired under certain work conditions, and it is possible grouping them into 3 or 7 categories. It is worth mentioning that the dataset examined here is composed of the same signals used by previous works aiming at comparing results. The aforecited kernel-methods (Support Vector Machine and Radial Basis Function Kernel) classified, with a high success rate, the output of detrended fluctuation analysis (DFA) of vibration signals according to their respective working conditions. In the classification of three major classes, the performance achieved by both classifiers reduces with the increase in the rotation speed. The best average success rates reached at 900 rpm, 1200 rpm and 1500 rpm were, respectively, 99.96% by RBFK, 99.24% by RBFK and 98.73% by SVM. For seven imbalance levels, both classifiers showed the best performance at 900 rpm again. In this case, the best rates reached 98.83% by RBFK. At 1200 and 1500 rpm, the rates are slightly different. © 2021 Elsevier Ltd