An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
Khaled Dhibi, Majdi Mansouri, Kais Bouzrara, Hazem Nounou, Mohamed Nounou
Abstract
The area of ensemble learning has gained a wide attention from the scientific research community. Ensemble methods are techniques that aim to improve the accuracy of results in models by combining multiple models instead of using a single model. The objective of this article is to develop intelligent fault detection and diagnosis (FDD) frameworks in order to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) systems based on improved ensemble learning approaches. Therefore, three ensemble learning-based fault detection and diagnosis techniques for Grid-Connected PV systems are proposed. First, an ensemble learning (EL) technique that combines predictions from Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Decision Tree (DT) is presented. The developed method will contribute to the reduction of the overall diagnosis error and will have the ability to combine various models. However, classical ensemble models ignore the time-dependence of PV measurements. In addition, the PV system data are frequently time-correlated. Accordingly, in the current work, the dynamic and multivariate nature of the measurements will be considered when designing the prediction models by using multivariate and dynamic techniques. To do these, kernel PCA (KPCA)-based EL and reduced KPCA (RKPCA)-based EL classifiers are developed. The two proposed techniques are addressed so that the features extraction and selection phases are performed using the KPCA and RKPCA models and the sensitive and significant characteristics are transmitted to the EL model for classification purposes. The presented results prove that the proposed methods offer enhanced diagnosis performances when applied to PV systems.