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In this paper, special attention is paid to the detection and diagnosis of various incipient faults of uncertain wind energy conversion (WEC) systems. The proposals will enhance the monitoring and diagnosis of the WEC system while taking into account the system uncertainties. The developed techniques are based on Support Vector Machine (SVM) model to improve the diagnosis of WEC systems. First, to deal with model uncertainties in WEC systems, the SVM will be extended to interval-valued data with the aim of achieving greater accuracy and robustness for these uncertainties. Then, to improve even more the performances of the developed interval-valued SVM, multiscale data representation will be used to develop multiscale extensions of interval-valued SVM. Next, as a feature selection tool, an improved extension of Artificial Butterfly Optimization (ABO) algorithm is used in order to extract the significant features from data and improve the diagnosis results of multiscale interval SVM. The proposed improved ABO method consists in reducing the number of samples in the training data set using the Euclidean distance and extracting the most significant features from the reduced data using ABO algorithm. This in turn plays a vital role in improving the accuracy using the multiscale interval-SVM method and reducing the computational and storage costs.

Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems