Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems

Abstract

One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learning technique aims to develop effective models that can provide a better rate of accuracy. Recently, numerous machine learning based ensemble models have been applied in FDD using different combination techniques. Ensemble method is a tool that merges several base models in order to produce one optimal predictive model. In this study, we propose six effective Ensemble Leaning (EL)-based FDD paradigms for uncertain Grid-Connected PV systems. First, EL-based interval centers and ranges and interval upper and lower bounds techniques are proposed to deal with PV system uncertainties (current/voltage variability, noise, measurement errors, $\ldots$ ). Next, in order to more improve the diagnosis abilities, two interval kernel PCA (IKPCA)-based EL classifiers are developed. The IKPCA-EL techniques are addressed so that the features extraction and selection phases are performed using the IKPCA models and the sensitive and significant interval-valued characteristics are transmitted to the EL model for classification purposes. Finally, the number of observations in the training data set is reduced using Hierarchical K-means techniques in order to overcome the problem of computation time and storage cost. Therefore, two interval reduced KPCA-EL techniques are proposed. The study demonstrated the feasibility and efficiency of the proposed techniques for fault diagnosis of Grid-Connected PV systems.

Keywords

• Uncertain systems
• ensemble learning
• fault diagnosis
• interval-valued data
• kernel principal component analysis (KPCA)
• grid-connected PV (GCPV)