Bypass diodes have been widely utilized in crystalline silicon (c-Si) photovoltaic (PV) modules to maximize the output of a PV module array under partially shaded conditions. A Schottky diode is used as the bypass diode in c-Si PV modules due to its low operating voltage. In this work, we systematically investigated the origin of bypass diode faults in c-Si PV modules operated outdoors. The temperature of the inner junction box where the bypass diode is installed increases as the ambient temperature increases. Its temperature rises to over 70 °C on sunny days in summer. As the temperature of the junction box increases from 25 to 70 °C, the leakage current increases up to 35 times under a reverse voltage of 15 V. As a result of the high leakage current of the bypass diode at high temperature, melt down of the junction barrier between the metal and semiconductor has been observed in damaged diodes collected from abnormally functioning PV modules. Thus, it is believed that the constant leakage current applied to the junction caused the melting of the junction, thereby resulting in a failure of both the bypass diode and the c-Si PV module.

The building-integrated photovoltaic (BIPV) system is provoking mention as a technology for generating the energy consumed in cities with renewable sources. As the number of BIPV systems increases, performance diagnosis through power-generation predictions becomes more essential. In the case of a colored BIPV module that has been installed on a wall, it is more difficult to predict the amount of power generation because the shading loss varies based on the entrance altitude of the irradiance. Recently, artificial intelligence technology that is able to predict power by learning the output data of the system has begun being used. In this paper, the power values of colored BIPV systems that have been installed on walls are predicted, and the system output values are compared. The current-voltage (I–V) curve data are measured to predict the power required changing the intensity of the irradiance, and the linear regression model is derived for the changes in the voltage and current at a maximum power operating point and during irradiance changes. To improve the power prediction accuracy by considering the shading loss of colored BIPVs, a new model is proposed via neural network machine learning (ML). In addition, the accuracy of the proposed prediction models is evaluated by comparing the metrics such as RMSE, MAE, and R². As a result of testing the linear regression model and the proposed ML model, the R² values for the voltage and current values of the proposed ML model were 5% higher for voltage and 2% higher for current. From this result, the proposed ML model of the RMSE about real power improved by more than 50% (0.0754 kW) compared to the simulation model (0.1581 KW). The proposed model demonstrates high-accuracy power estimations and is expected to help diagnose the performance of BIPV systems with colored modules.

In the potential induced degradation (PID) phenomenon, the output power of a photovoltaic (PV) module decreases due to the high potential difference between the PV system and the ground. This voltage forcefully moves the positive charge in the module to the surface of the solar cell. The accumulated charge leads to the performance deterioration of the module, namely, PID of the module. We conducted a study to accurately predict the output reduction of the module operating in various installation conditions coming from the PID phenomenon. We investigated the leakage current flowing through front glass laminated with encapsulation material simultaneously exposed to various performance conditions of the PV system, namely, relative humidity, temperature, and applied voltage, which have an important effect on the PID of the module. The degradation of the module coming from PID was calculated on the basis of the obtained leakage current. To confirm the calculated data, modules with one solar cell were manufactured and the power loss results of the modules' exposure to various PID generation experiments were compared with the expected results. The results showed that we could predict the degradation of the modules by PID within a 2% tolerance under the PV system installation conditions.

In PV (Photovoltaic) systems, the PV array is a structure in which many PV strings are connected in parallel. The voltage mismatch between PV strings, in which PV modules are connected in a series, occurs due to a voltage decrease in some modules. In this paper, research on the electrical characteristics of PV arrays due to a voltage mismatch was conducted. Considering the voltage mismatch, experiments on partial shading, the non-uniformity of irradiance, and the failure of bypass diodes were conducted on the PV module level. It was confirmed that the open-circuit voltage greatly decreased due to the failure of bypass diodes, which is among the causes of voltage mismatch. From the simulation results at the PV array level, it can be seen that a reverse current flowed into the low-potential string, which includes PV modules, causing the failure of the bypass diodes. Measuring the reverse current at one low-potential string, it was found that, in four parallel circuits, the reverse current was 12 A. For this reason, in large PV plants, an overcurrent can flow into the fuse due to the potential difference between strings, causing an output decrease of PV plants and the burnout of fuses.