Print Email Facebook Twitter Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network Title Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network Author Burbano, Rudy Alexis Guejia (University of Salerno) Petrone, Giovanni (University of Salerno) Manganiello, P. (TU Delft Photovoltaic Materials and Devices) Date 2021 Abstract In this paper, an artificial neural network (ANN) is used for isolating faults and degradation phenomena occurring in photovoltaic (PV) panels. In the literature, it is well known that the values of the single diode model (SDM) associated to the PV source are strictly related to degradation phenomena and their variation is an indicator of panel degradation. On the other hand, the values of parameters that allow to identify the degraded conditions are not known a priori because they can be different from panel to panel and are strongly dependent on environmental conditions, PV technology and the manufacturing process. For these reasons, to correctly detect the presence of degradation, the effect of environmental conditions and fabrication processes must be properly filtered out. The approach proposed in this paper exploits the intrinsic capability of ANN to map in its architecture two effects: (1) the non-linear relations existing among the SDM parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I−V curves and, consequently, on the SDM parameters. The ANN architecture is composed of two stages that are trained separately: one for predicting the SDM parameters under the hypothesis of healthy operation and the other one for degraded condition. The variation of each parameter, calculated as the difference of the output of the two ANN stages, will give a direct identification of the type of degradation that is occurring on the PV panel. The method was initially tested by using the experimental I−V curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental I−V curves. Subject Neural network applicationPhotovoltaic diagnosisPhotovoltaic modeling and parameters identification To reference this document use: http://resolver.tudelft.nl/uuid:e1f3b33f-2ca3-4df2-9274-b8b7e4583925 DOI https://doi.org/10.3390/app11198943 ISSN 2076-3417 Source Applied Sciences, 11 (19), 1-27 Part of collection Institutional Repository Document type journal article Rights © 2021 Rudy Alexis Guejia Burbano, Giovanni Petrone, P. Manganiello Files PDF applsci_11_08943.pdf 1.22 MB Close viewer /islandora/object/uuid:e1f3b33f-2ca3-4df2-9274-b8b7e4583925/datastream/OBJ/view