# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 4 Description of Experimental _I-V_ Curves Database  
For covering as much as possible the different outdoor operating conditions of real PV arrays, a large database of experimental data is selected. The _I-V_ curve dataset provided by the National Renewable Energy Laboratory (NREL) was used at the beginning to develop the proposed method. NREL has a public database with data measured for flat-plate photovoltaic (PV) modules installed in three different cities in the USA (Cocoa-Florida, Eugene-Oregon, and Golden-Colorado). The experimental process collected PV module current-voltages curves and meteorological data samples from 2010 until 2014 [31]. The work employed different PV technologies, such as single-crystalline silicon (c-Si), multi-crystalline silicon (m-Si), cadmium telluride (CdTe), copper indium gallium selenide (CIGS), amorphous silicon (a-Si) tandem and triple junction, amorphous silicon/crystalline silicon or heterojunction with intrinsic thin layer (HIT), and amorphous silicon/microcrystalline silicon. The database does not report specific commercial or manufacturer information for avoiding any legal conflict. To describe the procedure proposed in this manuscript, without the loss of generality, the multi-crystalline silicon PV module information is used. The variables extracted and used for the procedure are the plane-of-array irradiance (W/m\({}^{2}\)), the PV module back surface temperature (\({}^{\circ}\)C), and the corresponding current-voltage curve represented by a number of points ranging from 180 to 190 samples, depending on the voltage resolution settled on the tracer device. Table 1 shows the features and ranges of measurements of the panel chosen for performing the current analysis. The irradiance and temperature ranges also have a high impact over the performance of the PV model. From the literature, it is well known that the single diode model is not suitable for characterizing the PV devices at low irradiance values [32]. For this reason, in this work, we refer to irradiance values in the range from medium to high irradiance, and only _I-V_ curves acquired with a irradiance level above 200 [W/m\({}^{2}\)] are used. When it comes to the temperature, the single-diode model has no particular restrictions about the ranges. Therefore, there are no restrictions about the ranges of temperature. This work takes _I-V_ curves acquired over a wide temperature range of [20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65] \({}^{\circ}\)C degrees. High quality datasets is a key factor for training the ANN efficiently. To achieve this, the data must first be collected and cleaned to remove errors (bad data), outliers, and samples with excessive noise. If these practices are skipped or poorly executed, it becomes difficult for the ANN to detect the true underlying models. In certain cases, partial shading or measurement issues in the tracer device provide _I-V_ curves shapes that generate wrong SDM parameters. For this reason, the _I-V_ curves in the NREL database are preliminary analyzed, and the ones having an abnormal profile are discarded. At the end, more than 20,000 _I-V_ curves are available in the filtered database. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Technology & Multi-crystalline silicon \\ Number of cells & 36 \\ Module Area & 0.647 [m\({}^{2}\)] \\
**STC Parameters** & \\ \(I_{sc}\) & 4.937 [A] \\ \(V_{cc}\) & 21.357 [V] \\ \(I_{mp}\) & 4.533 [A] \\ \(V_{mp}\) & 17.072 [V] \\ Plane of Array irradiance & (200–1300) [W/m\({}^{2}\)] \\ Range of Temperature & (20–65) [\({}^{\circ}\)C] \\ Number of points for each _I-V_ curve & [180; 181; 182; 183; 184; 185; 186; 187; 188; 189; 190] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the PV panel under study and ranges of the data measured.