- In this work, the factors leading to string outages are examined, and an enhanced method for detecting faults at the sub-string level is presented. Utilizing GPT4-o to analyze O&M reports of 5089 photovoltaic plants, we classified outages according to the affected components and the underlying origin, identifying the most frequent string fault causes. An approach employing CUSUM Charts is introduced to identify substring outages within PV systems effectively. The methodology utilizes fundamental field data that is commonly available in practice. A filtering approach, combined with the use of CUSUM control charts, minimizes false positives, ensuring that only consistent underperformance is flagged as an out-age. The methodology returns far fewer false positives and more stable error intervals for substring outages than a former monitoring approach. Overall, the study demonstrates a significant improvement in detecting substring outages. The advanced methodology enables more effective O&M for PV plants, where substring outages are reliably identified after a short detection time.
