Abstract
Ensuring the validity of measurements in wind energy systems (WES) is a challenging task in system diagnosis and data validation. This work, therefore, elaborates on the development of new approaches aimed at improving the operation of WES by developing intelligent and innovative fault diagnosis frameworks. Therefore, an enhanced particle swarm optimization (PSO), data reduction, and interval-valued representation are proposed. First, a feature selection tool using PSO Algorithm is developed. Then, in order to maximize the diversity between data samples and improve the effectiveness of using PSO algorithm for feature selection, the Euclidean distance metric is used in order to reduce the data and maximize the diversity between data samples. Finally, PSO and RPSO-based interval centers and ranges and upper and lower bounds techniques are developed to deal with model uncertainties in WES. The last retained features from the proposed PSO-based methods are fed to the neural network (NN) classifier. The proposed methodology improves the diagnosis abilities, reduces the computation time, and decreased the storage cost. The presented experimental results prove the high performance of the suggested paradigms in terms of computation time and accuracy.