Novel Method to Detect Faults in Photovoltaic Systems

Researchers from Lanzhou University of Technology developed a fault-detection method based on voltage and current observation and evaluation

Depleting fossils fuels and increasing concerns regarding global warming has led to adoption of solar energy. Photovoltaics (PV) is the conversion of light into electricity using semiconducting materials that exhibit the photovoltaic effect. A photovoltaic system employs solar panels, each comprising a number of solar cells. However, the efficacy of the system is affected by complex and changeable climate conditions. Moreover, faults in the direct current side of a PV system are challenging to avoid. Some of the techniques used to detect fault are based on thermal infrared detection, time domain reflectometry, artificial intelligence algorithm, and mathematical model analysis methods.

Now, a team of researchers from Lanzhou University of Technology developed a fault-detection method to detect the potential faults and fault types of the PV array under various climate conditions. The method is based on voltage and current observation and evaluation. The approach is used to detect faults relating open circuit, short circuit, partial shading and degradation of the PV array in a PV system. The approach can also be used to identify variable shading faults in some special situation. The case in which degradation faults lead to increasing the series resistance of the degradation PV modules is considered for the study.

The team modelled a PV system including a fault detection unit on the MATLAB/Simulink platform to inspect the performance of the proposed method for PV array fault detection. The real-time output voltage and current values of the PV array are the inputs of the fault detection unit and the voltage and current indicators under fault-free conditions, the fault detection thresholds and the real-time voltage and current indicators are the outputs. The method can detect the potential faults of the PV array under a test condition and recognize the type of detected faults. The team conducted eight fault types in the PV array to assess the strength of the fault detection method. In further research, the team is expected to focus on further enhancing the fault-detection method.