Faults diagnosis in a photovoltaic system based on multivariate statistical analysis

Abstract

ABSTRACT Improving the efficiency of photovoltaic (PV) systems has gained priority in current research due to the large volumes of PV panels installed. Moreover, the remarkable efforts made to investigate different methods of diagnosing PV failures have multiplied, giving additional impetus to research on the efficiency of PV systems. However, most of these methods are limited in the number of faults that can be identified; some are expensive and complex, and others require huge amounts of data to train. In this paper, a simple and robust multivariate statistical analysis method is proposed for the diagnosis and identification of faults in a PV system. From the multitude of data that can be collected on a PV system in operation or available on most new inverters on the market, the method used here is based on kernel principal component analysis, which looks at and analyses the variance between these data. Combined with the Hotelling statistic () and Squared Prediction Error (SPE) index associated with Rolle’s theorem, this analysis identifies six operating states of the PV system: normal operation, short-circuited panels, open-circuit panels, partially shaded panels, serial resistance degradation, and MPPT error. 200 samples of different faults at different irradiance have been generated between an irradiance from 50 to 500 w/m2. The accuracy rate was 64% for the degradation of series resistance, 91.11% for partial shading, 100% for open circuit, 100% for short circuit, and 100% for MPPT error. The various results obtained first from a Matlab-Simulink model, and then from a real system of 18 modules, demonstrate the efficiency and performance of the proposed algorithms.