Enhancing Real-Time Fault Detection in Electrical Grids Using Hybrid EnsembleBoost over Wireless Networks

This study presents an innovative method for real-time fault detection in electrical grids by integrating Gradient Boosting Decision Trees (GBDT) with ensemble learning, termed "EnsembleBoost," and deploying it over wireless communication channels. Traditional fault detection systems often encounter challenges like latency and scalability due to the intricate nature of grid operations and limitations in wired communication. To address these issues, we propose a hybrid approach that combines GBDT's proficiency in capturing complex fault patterns with wireless technology's agility. Trained on historical sensor data, the EnsembleBoost model demonstrates exceptional accuracy in identifying anomalies inherent in electrical grid operations. Deployed across strategically positioned wireless nodes within the grid, our distributed fault detection system can promptly detect faults in real time. Extensive simulations and experiments conducted on a real-world grid testbed validate the effectiveness of our approach, achieving a fault detection accuracy of 95.60% and reducing latency by 35% compared to conventional methods. This research provides a promising solution for enhancing smart grid management through the synergistic integration of GBDT and wireless communication technologies.