AI-driven Solutions for Autonomous Maintenance and Fault Detection in Electrical Power Grids

Efficient and reliable power grids are crucial for the functioning of modern society. However, increasing energy demand and renewable sources can lead to power failures. Traditional methods struggle. AI, particularly Random Forest Classifiers, offers a promising approach. These classifiers analyse vast amounts of grid data to identify patterns indicative of faults. This allows for real-time anomaly detection. This research investigates the efficacy of Random Forest models for fault classification, focusing on optimising data preprocessing and collection. Transparency in data collection and model development is crucial for data privacy. While simulated environments pose challenges for early fault detection, real-world data presents even greater complexities. The effectiveness of Random Forest Classifiers in early fault detection is critical for grid reliability, reducing power outages, and enhancing long-term operational efficiency.