Fast Model-Based Fault Detection in Single-Phase Photovoltaic Systems

We present a model-based approach for the instant detection of faults on the DC side of photovoltaic (PV) systems. The algorithm does not identify the faults itself, but estimates the nominal PV system behavior, i.e. system parameters, using simple PV and line models. Sudden deviations from the expected model behavior serve as an indicator for the ignition of a fault. To ensure that the PV model parameters can be estimated, an identifiability analysis has to be performed. The performance of the algorithm is demonstrated exemplarily by the detection of serial electric arcs in PV systems. Measurement results show that all series arc faults are successfully detected. There are no false detections due to maximum power point tracking (MPPT) operations or environmental influences like shading, changes in solar irradiation, etc. The main advantages of the presented method are less computational effort, resulting in very fast detection times, and its flexible integration into existing systems.