Enhanced Recurrent Neural Network for Fault Diagnosis of Uncertain Wind Energy Conversion Systems

In this paper, new fault detection and di-agnosis (FDD) techniques dealing with uncertainties in wind energy conversion (WEC) systems are proposed. The uncertainty is addressed by using the interval-valued data representation. The main contributions are twofold: first, to simplify the Recurrent Neural Network (RNN) model in terms of training and computation time and storage cost as well, a reduced version of RNN is proposed. Reduced RNN is established on the H-K-means algorithms to treat the correlations between samples and extract a reduced number of observations from the training data matrix. The main idea behind using H-K-means algorithms for dataset size reduction is to simplify the RNN model in terms of training and computation time. Second, two reduced RNN-based interval-valued data techniques are proposed to distinguish between the different WEC system operating modes. Therefore, two reduced RNN-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with the WEC system uncertainties. The presented results confirm the high feasibility and effectiveness of the proposed FDD techniques.