Online model-based fault detection for grid connected PV systems monitoring
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Applied Mathematics and Computational Science Program
KAUST Grant NumberOSR-2015-CRG4-2582
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AbstractThis paper presents an efficient fault detection approach to monitor the direct current (DC) side of photovoltaic (PV) systems. The key contribution of this work is combining both single diode model (SDM) flexibility and the cumulative sum (CUSUM) chart efficiency to detect incipient faults. In fact, unknown electrical parameters of SDM are firstly identified using an efficient heuristic algorithm, named Artificial Bee Colony algorithm. Then, based on the identified parameters, a simulation model is built and validated using a co-simulation between Matlab/Simulink and PSIM. Next, the peak power (Pmpp) residuals of the entire PV array are generated based on both real measured and simulated Pmpp values. Residuals are used as the input for the CUSUM scheme to detect potential faults. We validate the effectiveness of this approach using practical data from an actual 20 MWp grid-connected PV system located in the province of Adrar, Algeria.
CitationHarrou F, Sun Y, Saidi A (2017) Online model-based fault detection for grid connected PV systems monitoring. 2017 5th International Conference on Electrical Engineering - Boumerdes (ICEE-B). Available: http://dx.doi.org/10.1109/ICEE-B.2017.8192117.
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2582.