# Early Detection of Photovoltaic Panel Degradation through Artificial Neural Network
## 3 Description of the Proposed ANN Architecture  
In the previous section, the relation among PV panel degradation and SDM parameters variation was put into evidence by analyzing the \(I\)-\(V\) curves. On the other hand, the parameters of the PV equivalent circuit change with respect to the irradiance and the temperature of the solar cells. The relationship between them is nonlinear and cannot be easily expressed by analytical equations; nonlinear regression methods can fail when any preliminary information about the input-output relationships is provided. Many papers have tried to characterize such behavior. However, it is strongly dependent on the PV panel under test, and no general rules can be applied. Some examples are reported in [29]. In [30], the authors highlight that the fitting curve, which maps each SDM parameter as a function of the environmental condition, also varies along the seasons, due to the influence of the variable weather and environmental conditions. As a consequence, a reliable identification of PV degradation phenomena cannot be achieved if relationships among SDM parameters and the environmental condition are not properly accounted for. The capability of the ANN to train non-linear and unknown relations among variables and to generalize these relationships when new input is provided to the ANN, is exploited for solving this task. Since ANN does not require knowledge of the internal system parameters, it implies reduced computational effort and represents a compact solution for multivariable problems. It is also a good candidate to be implemented on embedded system for online operation. Figure 3 shows the flow chart of the procedure proposed in this paper. In particular, it describes the main steps for selecting the ANN input and target datasets and performing the ANN training phases. In the first stage, the ANN predicts the equivalent circuit parameters by only measuring the irradiance and temperature. Such parameters are assumed to be reference values for the healthy operating condition. Indeed, for training this ANN, a proper number of healthy _1-V_ curves, acquired under different environmental conditions, are selected, and the corresponding SDM parameters are used as the target dataset. As shown in [26], ANN can provide a very good estimation of healthy SDM parameters for every environmental condition. This SDM parameters estimation is used as the baseline for detecting the possible presence of degradation. In a second stage, a more complex ANN architecture is trained to account for the different types of degradation. For achieving this task, a modified set of _1-V_ curves is generated by using the single diode model with different sets of parameters that are associated to realistic degradation phenomena. In this stage, the ANN receives as input not only the irradiance and cell temperature, but also some points of the _1-V_ curve, which are necessary for taking into account the modification of the _1-V_ curve shape due to the degradation. It is worth noting that in this paper, only three points around the maximum power point are used. This allows to monitor the PV source's state of health during normal operation without the need for a complete scan of the _1-V_ curve: measurement of the voltage and current around the MPP suffices for the detection of degradation. More details on the generation of degraded datasets are given in Section 4.2. The two training phases are completely independent, having in common only part of the input data (_G_ and _T_) and providing two sets of SDM parameters. One output represents the vector of estimated SDM parameters under the hypothesis that the PV panel is not degraded--in a healthy condition--and the other one is the vector of estimated SDM parameters associated to the real state of health. The difference between the two estimations gives a measure of which parameter is changing and consequently, which type  
Figure 3: Flow chart for training the proposed ANN architecture. of degradation is occurring inside the PV panel.