A Digital Twin Approach for Fault Diagnosis in Distributed Photovoltaic Systems

Abstract

Rooftop and building-integrated distributed photovoltaic (PV) systems are emerging as key technologies for smart building applications. This paper presents the design methodology, mathematical analysis, simulation study, and experimental validation of a digital twin approach for fault diagnosis. We develop a digital twin that estimates the measurable characteristic outputs of a PV energy conversion unit (PVECU) in real time. The PVECU constitutes a PV source and a source-level power converter. The fault diagnosis is performed by generating and evaluating an error residual vector, which is the difference between the estimated and measured outputs. A PV panel-level power converter prototype is built to demonstrate how the sensing, processing, and actuation capabilities of the converter can enable effective fault diagnosis in real time. The experimental results show detection and identification of ten different faults in the PVECU. The time to fault detection (FD) in the power converter and the electrical sensors is less than 290 $\mu$ s and the identification time is less than 4 ms. The time to FD and identification in the PV panel are less than 80 ms and 1.2 s, respectively. The proposed approach demonstrates higher fault sensitivity than that of existing approaches. It can diagnose a 20% drift in the electrical sensor gains and a 20% shading of a solar cell in the PV panel.