This work is aimed at detecting degradation phenomena on photovoltaic (PV) panels working under real outdoor conditions by using the impedance spectroscopy technique. The impedance is measured by leaving the PV panel in the real-operative status corresponding to the maximum power point without altering its power production. Since variations on the series resistance are associated with multiple degradation phenomena, this study seeks to estimate changes in this parameter using a dynamic model fitted to the experimental impedance measurements. An external resistor is added in series to the PV panel for emulating the degraded conditions. Hence, the PV panel impedance measurements are compared in nominal and degraded conditions. The results show that the dynamic model provides higher accuracy in comparison with the series resistance variation identified through the single-diode model, which is the most usual approach. Thus, it is demonstrated the feasibility of detecting degradation effects by using the dynamic model of PV panels working under outdoor conditions.

This paper presents a communications test-rig based on a customized Intelligent Electronic Device (IED) for its use in a laboratory-scale microgrid. The laboratory setup for this work uses the following three types of subsystems: distributed energy resources DERs (solar, wind), energy storage (batteries) and loads (active, reactive, linear and nonlinear). A Raspberry Pi computer, using the Linux operating system, allows programming the algorithms needed by the communications platform. The protocol employed for data exchange, between the different subsystems and the central supervisor module, is the IEC 61850 standard. Three experimental test cases serve to show the proposed IEDs performance. The first test transfers data between a solar source and a client station. The second test transfers data, in distributed mode, between four nodes associated to different generation sources. The last test shows a secondary control using two experimental inverters operating in parallel connection. The IEDs communicate the compensating values for regulating frequency and voltage in the system composed of a master and one or more slave inverters.

A photovoltaic array including several modules in series may show mismatching due to discrepancies among the module conditions, mainly due to partial shadowing. Therefore, the shape of the current–voltage curve deeply changes with respect to the one corresponding to uniform operation. This paper shows that a small set of points around the maximum power allows to detect the occurrence of the mismatching. This approach exploits such a limited information to detect if the module is subjected to mismatching, so that the adoption of a global maximum power point tracking algorithm can be avoided. The curvature change is identified by using different machine learning techniques: decision trees, multilayer perceptrons, radial basis functions, and support vector machines. To reduce the classification error, before the fitting of the models, we implement a novel process of selection of the training samples based on a self–organizing map. This procedure makes easier the optimization of the number of hidden neurons. The support vector classifier and the multilayer perceptron with one hidden layer outperform the other approaches, being the former better than the last for extreme mismatching. However, the prediction time of this multilayer perceptron is significantly smaller than the required by the support vector machine.

This article investigates the use of Impedance Spectroscopy (IS) to identify mismatches in Photovoltaic (PV) panels/string/array, particularly for detecting partial shading effects. The findings confirm IS as a promising tool for on-field applications, especially in small-scale urban power plants where partial shading is a common issue. An analysis of several experimental impedance spectra measured with PV panels operating at their Maximum Power Point (MPP), with controlled partial shading conditions, reveals that partial shading induces a characteristic double-arc deformation. This feature could be leveraged in a diagnostic tool for mismatch detection. The key advantage of this approach is its ability to perform IS measurements at MPP, preserving the normal operating conditions of the PV panels/string/array. Additionally, IS requires minimal modifications to the system's architecture and integrates easily into existing power electronics interfaces. To validate the consistency of impedance spectra under partial shading, experimental data were compared with two models: a Constant Phase Element (CPE) model adapted for PV strings in non-uniform conditions and an enhanced Single-Diode Model (SDM) developed for SPICE3-based simulations. The results confirm that IS successfully adjusts the models and detects mismatches in both experimental and simulated scenarios, demonstrating its scalability and effectiveness as a state-of-the-art PV diagnostic tool.

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 $I\text{-}V$ 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 $I\text{-}V$ curves provided by the NREL database, where the degradation was introduced artificially, later tested by using some degraded experimental $I\text{-}V$ curves.