Quickest Fault Detection in Photovoltaic Systems

Photovoltaic (PV) systems play an important role in contemporary electricity production as a ubiquitous renewable energy source. However, the performance of a PV system is susceptible to unexpected faults that occur inside its various components. In this paper, we propose a quickest fault detection algorithm for PV systems under the sequential change detection framework. In particular, multiple meters are employed to measure different output signals of the PV system. The time correlation of the faulty signal and the signal correlation among different meters are exploited by a vector AR model in modeling the post-change signal. In order to tackle the difficulty that no prior knowledge about the fault is available, we develop a change detection algorithm based on the generalized local likelihood ratio test. Extensive simulation results demonstrate that the proposed method achieves high adaptivity and fast detection in dealing with various types of faults in PV systems.