Multivariate feature extraction based supervised machine learning for fault detection and diagnosis in photovoltaic systems.

• A principal component analysis (PCA)-based supervised machine learning (SML) method is developed. • PCA-based SML is proposed to enhance fault detection and diagnosis (FDD) of photovoltaic (PV) systems. • The developed FDD approach uses feature extraction and selection, and fault classification tools. • The detection performance is studied using several PV system faults. • The results show good diagnosis efficiency and higher classification accuracy in PV systems. Fault detection and diagnosis (FDD) in the photovoltaic (PV) array has become a challenge due to the magnitudes of the faults, the presence of maximum power point trackers, non-linear PV characteristics, and the dependence on isolation efficiency. Thus, the aim of this paper is to develop an improved FDD technique of PV systems faults. The common FDD technique generally has two main steps: feature extraction and selection, and fault classification. Multivariate feature extraction and selection is very important for multivariate statistical systems monitoring. It can reduce the dimension of modeling data and improve the monitoring accuracy. Therefore, in the proposed FDD approach, the principal component analysis (PCA) technique is used for extracting and selecting the most relevant multivariate features and the supervised machine learning (SML) classifiers are applied for faults diagnosis. The FDD performance is established via different metrics using data extracted from different operating conditions of the grid-connected photovoltaic (GCPV) system. The obtained results confirm the feasibility and effectiveness of the proposed approaches for fault detection and diagnosis. [ABSTRACT FROM AUTHOR]