Performance and Adaptability Testing of Machine Learning Models for Power Transmission Network Fault Diagnosis With Renewable Energy Sources Integration

Numerous research works establish the high efficacy of Machine Learning (ML) based power system fault diagnosis over conventional analytical methods. The ongoing integration of renewable energy sources (RES) into the existing transmission networks alters the system topology, potentially resulting in significant changes in fault signatures depending on the size of the newly added RES. However, there is a notable absence of studies in the literature analyzing the impact of new RES integration on the fault diagnosis performances of ML models. Therefore, to assess the fault classification and localization performance of potential ML models, this paper proposes to analyze two practical scenarios arising from new RES integrations: 1) when no-fault data is available for the changed system and 2) when the changed system data is available over time. The proposed performance and adaptability testing of potential ML models has been conducted by optimally integrating different sizes of RES into 'IEEE 9-Bus System'. The integrated solar-based RES has been modeled incorporating standard temperature and irradiance variations. A diverse fault database was generated considering actual field variations of fault attributes. Impact analysis revealed significant degradation in the fault diagnosis performances of all tested models post RES integrations. The adaptability testing was performed by extensive analysis of the learning trends of ML models with gradual data availability. The proposed Bayesian ridge regression has emerged as the fastest learning model for locating transmission line faults, whereas XGBoost, Extra Tree, and Random Forest classifiers gave comparable results for fault classification.