Reduced Gaussian process regression based random forest 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. First, two RGPR models are proposed to deal with WEC features extraction and selection. The proposed RGPR models extract 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). Next, in order to classify the WEC faults and improve the diagnosis abilities, RF classifier is developed. The proposed RGPRHKmeans-RF and RGPRED-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. The open-circuit, wear-out, and short-circuit are the three transistor faults 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.

Automated summary

This paper proposes a novel Reduced Gaussian Process Regression (RGPR)-based Random Forest (RF) technique for fault detection and diagnosis of wind energy conversion (WEC) systems. The method reduces computation burden, boosts classification speed and accuracy, and demonstrates effectiveness and robustness in fault detection in WEC systems.