This PV system is capable of studying faults among modules with different array configurations. In order to test the ability of the proposed approach to detect and locate the faults and identify the fault types, a series of line-line faults within the string are used in the simulations.
The fault detection methods for the PV system are classified in the visual (discoloration, browning, surface soiling, and delamination), thermal (thermal extraordinary heating), and electrical (dark/illuminated I - V curve measurement, transmittance line diagnosis, and RF measurement).
The solar PV panels are monitored and controlled using IoT nodes in smart monitoring systems. The earliest smart monitoring devices were created in Japan, and they included microprocessors, network radios, relays for connecting or obstructing panels, and sensors.
The condition monitoring and fault detection in large-scale solar farms is essential to ensure the longevity of equipment and maximized power yield. The large-scale solar farms comprise of thousands of solar panels that are spread over many hectares of land.
Fault analysis in the solar PV arrays is a fundamental task to eliminate any kind of dangerous and undesirable situations arising in the operation of PV array due to the presence of faults. They must be detected and cleared off rapidly.
The photons emitted by this strategy which near wavelengths beyond 850 nm can be imaged using capable Si-CCDs cameras . In recent times, smart systems combining AIs and the IOTs have been developed for monitoring, diagnostics and fault detections of PV solar power plants.