Development of Fault Diagnosis Program for reducing Power Loss Cost of the Photovoltaic Power System using Actual Operation Data

Countries are looking for a pathway toward a sustainable transition from fossil-based to less or zero-carbon sources in energy sector in order to reduce its impact from climate change. Among different renewable resources, photovoltaic power system (PV) is considered as one of the most promising technology, which has the biggest potential for increasing renewable energy. However, its profit can be varied depending on operation and management, which possibly causes performance degradation and safety issues due to faults. Therefore, we have collected actual operation data from the PV monitoring system which located in Changwon, Gyeong-nam province during one year. While most of the PV system has security function which is limited to its inverter protection, in this study, based on fault data software program is developed for fault diagnosis in order to increase robustness of the PV system and minimize operation cost. Also, the program comprises machine learning algorithm based on fault data to classify its types of faults. It also presents economic loss of each PV module considering mean time to repair (MTTR) occurred from the event of faults in the PV system. As a result, the program helps faster fault diagnosis of the PV system and decreasing overall operation cost for the system operator.