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.
Highlights
The penetration of photovoltaic (PV) generation in the urban environment is significantly growing, owing to its ability to reduce the power bills of owners and support the grid with local generation [1].In this scenario, PV systems degradation and failures are less tolerated since do they reduce the return on investment, but they can lead to a grid power imbalance if the actual energy production is different from the expected energy production that comes, for instance, from a digital twin of the system.One of the key factors for increasing PV system reliability and its service life is to develop methodologies and technical solutions for the accurate monitoring of the state of health of PV panels
The approach exploits the intrinsic capability of artificial neural network (ANN) to map in its architecture two effects: (1) the non-linear relations existing among the single diode model (SDM) parameters and the environmental conditions, and (2) the effect of the degradation phenomena on the I-V curves and, on the SDM parameters
The multilayer perceptron (MLP) artificial neural network is used for isolating faults and degradation phenomena affecting photovoltaic panels
Summary
The penetration of photovoltaic (PV) generation in the urban environment is significantly growing, owing to its ability to reduce the power bills of owners and support the grid with local generation [1].In this scenario, PV systems degradation and failures are less tolerated since do they reduce the return on investment, but they can lead to a grid power imbalance if the actual energy production is different from the expected energy production that comes, for instance, from a digital twin of the system.One of the key factors for increasing PV system reliability and its service life is to develop methodologies and technical solutions for the accurate monitoring of the state of health of PV panels. Especially in urban area, residential PV plants have a high probability to be subject to panel mismatches, partial shading, hot spots, and mechanical stress, which accelerate the degradation phenomena Another example is shown in [2], where the authors prove that the combined effect of PV delamination, water penetration into the 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 keep the PV plant up and running, thereby increasing the PV plant’s energy yield over the system lifetime
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