Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems

This paper proposes an effective fault detection and diagnosis (FDD) paradigm in Wind Energy Converter (WEC) Systems. The developed FDD frame-work merges the benefits of kernel principal component analysis (KPCA) model and bidirectional long short-term memory (BiLSTM) feature classifier. KPCA is used to extract and select the most effective features. While, BiLSTM is used for classification purposes. The proposed KPCA-based BiLSTM approach involves two main steps; feature extraction and selection and fault classification. It is tackled in such a way that KPCA model is developed in order to select and extract the more efficient features where the final features are fed to BiLSTM to distinguish between different working modes. Different simulation scenarios are considered in this study in order to show the robustness and performances of the developed technique when compared to the conventional FDD methods.