# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
###### Abstract
The use of photovoltaic models in combination with AI methods has been already proposed in the literature for PV faults detection. In [9], an artificial neural networks (ANN) is firstly trained with a numerical simulation, provided by a PV model, for the classification and isolation of eight types of faults, and then used on field measurements to identify possible faulty operating conditions. A field programmable gate array (FPGA) implementation of the proposed method is also proposed in the paper for the online operation. In [10], the kernel-based extreme learning machine (KELM) is employed to train the single hidden-layer feed-forward neural network (SLFN) to classify the degradation fault, short-circuit fault, open-circuit fault and partial shading faults in PV arrays. The SLFN needs as input electrical values taken from the _I-V_ curve, environmental conditions and the SDM parameters previously calculated starting from the whole _I-V_ curve. An ANN with a radial basis function (RBF) requiring only irradiance and PV output power as input is developed in [11]. The results obtained through the testing of the developed ANN on a PV installation of 2.2 kW capacity provide an accuracy of 97.9% in faults identifications. In this case, no model is used but it long-term data measurements are taken for reproducing on the PV installation different kind of faults. The ANNs training phase usually requires a large number of observations, which are not always available. This problem might be mitigated by using probabilistic neural networks (PNNs), which learn on-line with a small number of observations [12; 13]. The explosion of the IoT technologies is expected to enable, with an acceptable additional cost, the diagnosis at the PV panel level. Moreover, by exploiting edge computing sensors [14], it will be possible to elaborate the data on site and to transmit to the final user only synthetic information related to the state of health of each PV module. Module-level monitoring devices are already available in the market for monitoring and controlling a single PV panel, thereby improving the system performance and the planning of system's operation and maintenance activities [15]. Such devices are also able to perform _I-V_ curve tracing but the final user is in charge of the analysis of these data. Nevertheless, the offline data analysis is time consuming and requires an operator with a specific PV background. Moreover, the types of faults and degradation mechanisms that can be identified are very limited. To solve this drawback, in [16], it is proposed an automatic fault detection method that elaborates online the \(I\)-\(V\) curves of PV panels. The diagnosis is focused on the identification of current mismatch due to partial shading, hot spots, and cell cracks. The method calculates the concavity and convexity along the \(I\)-\(V\) curves because the three analyzed faults produce steps on the \(I\)-\(V\) curve; thus, not trivial data processing is required for proper detection of the occurring fault. Differently from the previous works, which are mainly focused on faults classification or partial shading identification, the ANN-based method proposed in this paper is aimed at estimating PV degradation through the identification of SDM parameters values that can be easily related to the PV panel state of health. The approach 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 joint elaboration of these two relationship allows to quantify the PV degradation due to aging, corrosion, cracks, or hot spots, among others.