Abstract:
Photovoltaic (PV) module faults have harmful effects on both the efficiency of power
generation and overall safety. Among these faults, current mismatch is the most
common type, leading to a decrease in output current and causing distinct steps in the
(current and voltage) I-V representative curves as well as multiple spikes in the P-V
curves. Consequently, the power output of PV units is significantly impacted. This
research delves into the scrutiny of faulty PV units in real-world PV power sites,
specifically focusing on current inequality faults resulting from partial shadowing, hot
spots, and cracks. There are various techniques used to detect the current faults. These
techniques are ground fault detection and interruption (GFDI), over current protection
(OCP), Insulation monitoring devices (IMD), and Arc fault current interruption
(AFCI). Other than these devices various classification algorithms have also been
developed which can be employed to classify the detected PV faults while the system
is running. In this research the dataset from previous research is used to train
regression tree, SVM, and logistic regression classifiers. Amongst these classifiers,
regression tree classifier has presented an accuracy of up-to 99%, while the previous
research presented an accuracy of 98%. This research distinguishes between different
fault features within the I-V curve steps and proposes computational analytics and
statistical techniques for diagnosing PV unit mismatch faults. The inclusion of PV
system reduces the carbon footprint paving ways to green energy, in addition to
saving fuel and generation costs on a yearly basis. As such this research aligns itself
with the Sustainable Development Goals (SDGs) set by the United Nations.
