Integrating AI Models for Voltage and Current Monitoring in Autonomous Mobile Robots to Prevent Power System Blackouts

Continuous operations in extreme environments require a reliable energy supply for autonomous systems. The proposed research presents a novel AI-driven system that averts power system blackouts by utilizing autonomous robots trained in artificial intelligence. The robots monitor power system signals in real-time to detect anomalies while employing their intelligence to select optimal distribution methods. This research combined multiple machine learning models utilizing power system attack data from Mississippi State University and Oak Ridge National Laboratory to ascertain their efficacy in anomaly detection and enhancing energy resilience. In power system threat detection, the ExtraTree Classifier and Random Forest Classifier demonstrate optimal accuracy at 95%, confirming their ability to ensure energy stability. In addition to XGBoost (88%), Bagging (91%), and Decision Tree (86%), the ExtraTree Classifier demonstrates superior performance; however, Gradient Boosting (77%) and Logistic Regression (63%) need improvement to ensure reliable performance in critical applications. The proposed method implements XAI techniques to make the system explainable while improving decision-making reliability. Future research will build on this work by augmenting data availability, integrating renewable energy systems into the research framework, and enhancing self-healing capabilities and robotic mobility to further improve power grid resilience. The developed findings on AI-driven energy automation of this mathematical applicability exploration created scalable intelligent solutions to tackle power stability challenges in unpredictable environmental conditions.