HYBRID ML-BASED FAULT DETECTION IN RENEWABLE-INTEGRATED POWER GRIDS

Abstract

Integration of renewable generation into modern power grids brings new problems in the field of fault detection because now the sources are intermittent and accordingly it is more complex energy mixed. In a network including renewable resources, the conventional fault detection techniques cannot perform to optimum level as they can in constant practice. This work introduces a hybrid machine learning (ML) methodology that synergistically complements the best of both supervised classification algorithms and unsupervised anomaly detection technique to boost fault detection performance. The proposed framework trains a Random Forest, Support Vector Machine and k-Means clustering based ML models with historical operational data that include voltage, current, and frequency measurements. Fusion always makes the decision heavier based on detection robustness by different model outputs. Experiments in simulation and on real-world data show that our approach is more accurate, detects faults faster, and learns to adapt to new grid conditions better than single-model baselines. These findings showcase the efficacy of hybrid AI-based solutions in guaranteeing reliability and resilience within renewable-inclusive power systems.