Challenges Posed by Renewable Energy Source Integration to Machine Learning Based Power System Fault Diagnosis

Nowadays machine learning (ML)-based power system fault diagnosis is being researched to a great extent. ML techniques have many advantages over conventional fault diagnosis techniques. However, with the advent of renewable energy sources (RES) integration on a large scale it is necessary to study whether ML models will be able to adapt to new RES integration. In this paper, a performance investigation of a few ML techniques has been done for power system fault classification and localization on new solar PV plant integration to the IEEE 9 Bus system. The proposed performance investigation is an impact analysis of new RES integration on ML model performance when fault data for RES integrated system is unavailable. Thus, fault classification and localization of RES integrated power system faults are predicted from pre-trained ML models, that were trained from conventional power system fault data. The results revealed that ML models' performance degrades severely with RES integration.