# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
###### 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. photovoltaic diagnosis; neural network application; photovoltaic modeling; parameters identification +
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delaminated area and high string voltage operation leads to many failures in PV panels and inverters. Since the severity of delamination increases gradually, this phenomenon can be early detected so that the affected PV panel can be replaced with a new one. This preventive maintenance will preserve the inverter operation, avoid further damages and consequently, keep the PV plant up and running, thereby increasing the PV plant's energy yield over the system lifetime. PV modules also exhibit natural aging that reduces the annual PV energy production with a more or less flat degradation rate. For crystalline silicon PV modules, it was estimated a reduction of [0.8-0.9]% over the year [3]. An exhaustive review of degradation phenomena occurring in PV modules is reported in [4]. The early detection of PV degradation allows to make decisions about system maintenance and PV panel replacements. In some cases, it can prevent catastrophic consequences, such as fires. At present, there are many studies related to PV fault diagnosis, but most of them are focused at the PV system level. In [5], different methods are reviewed and discussed in detail by putting into evidence their feasibility, complexity, cost-effectiveness and generalization capability for large-scale integration. In [6], the discussion is focused on the use of artificial intelligence (AI) and Internet of Things (IoT) for the remote sensing of solar photovoltaic systems to improve the PV diagnosis. On the other hand, such models as the single-diode model (SDM), double-diode model (DDM), and triple-diode model (TDM) are widely used for the modeling, simulation, performance evaluation, and design optimization of PV systems as well as for monitoring and diagnosis purposes [7]. Indeed, the accurate parameter identification of the equivalent PV electrical model allows to study the characteristics of a PV source [8] in all operating conditions. Therefore, instead of analyzing the shape of each _I-V_ curve, it is easier to detect degradation by evaluating the variations of such parameters with respect to their values in healthy conditions.