Reduced GPR based RF Approach for Fault Diagnosis of Wind Energy Conversion Systems

This paper proposes a novel Reduced Gaussian Process Regression (RGPR)-based Random Forest (RF) technique (RGPR-RF) for fault detection and diagnosis (FDD) of wind energy conversion (WEC) systems. The statistical features, including the mean vector $M_{RGPR}$ and the variance matrix $C_{RGPR}$ , are computed using the RGPR model then fed to the RF algorithm for fault classification purposes. The proposed RGPR model extracts the most relevant information from the WEC system data while reducing the computation burden compared to the classical GPR model. The complexity reduction is ensured by the selection of the most effective samples through the dimensionality reduction (DR) metrics including Hierarchical K-means (HKmeans) clustering and Euclidean distance (ED). The proposed $\text{RGPR}_{HKmeans}$ -RF and $\text{RGPR}_{ED}$ -RF techniques boost the classification speed and accuracy using a reduced number of features where only the most relevant and sensitive characteristics are kept in case of redundancy. Three kinds of WEC system faults are considered in order to illustrate the effectiveness and robustness of the developed techniques. The obtained results show that the proposed RGPR-RF technique is characterized by a low computation time and high diagnosis accuracy (an average accuracy of 99.9%) compared to the conventional RF classifiers.