Fault Diagnosis in Photovoltaic Modules using a Straightforward Voltage-Current Characteristics Evaluation

Due to the growing demand in the electricity sector and the shift to the operation of renewable sources, the use of solar arrays has been at the forefront of the consumers' interests. In the meantime, since the production capacity of each solar cell is limited, in order to increase the production capacity of photovoltaic (PV) arrays, several cells are arranged in parallel or in series to form a panel in order to obtain the expected power. Short-circuit (SC) and open-circuit (OC) faults in the solar PV systems are the main factors that reduce the amount of solar power generation, which has different types. Partial shadow, cable rot, un-achieved maximum power point tracking (MPPT), and ground faults are some of these malfunctions that should be detected and located as soon as possible. Therefore, an effective fault detection strategy is very essential to maintain the proper performance of PV systems in order to minimize the network interruptions. The detection method must also be able to detect, locate, and differentiate between the SC and OC modules in irradiated PV arrays and non-uniform temperature distributions. In this work, based on the artificial intelligence (AI) and neural networks (NN), neutrons can be utilized, as they have been trained in machine learning process, to detect various types of faults in PV networks. The proposed technique is faster than the other artificial neural networks (ANN) methods since it uses an additional hidden layer that can also increase the processing accuracy. The output results prove the superiority of this claim.

Automated summary

Due to the increasing demand in the electricity sector and the shift towards renewable energy sources, the use of solar arrays has gained significant attention. To increase the production capacity, multiple solar cells are arranged in parallel or series to form a panel. Faults such as short-circuits and open-circuits in the PV systems can significantly reduce the amount of solar power generation. An effective fault detection strategy is essential to maintain the proper performance of the PV systems and minimize network interruptions. This study proposes an AI and NN-based detection method utilizing trained neurons to detect various types of faults in PV networks.