Author: Dr. Karan Malhotra, Er. Neha Raghavan.
Abstract: The application of Machine Learning (ML) in power grid operations has opened new avenues for predictive maintenance, fault detection, and demand forecasting. Traditional methods of load and fault prediction often fall short in adapting to complex and dynamic grid environments. ML algorithms leverage historical and real-time data to improve prediction accuracy, minimize downtime, and optimize resource allocation. This paper presents a comprehensive study on ML-based load and fault prediction techniques, discussing their implementation, benefits, and challenges. We explore supervised, unsupervised, and hybrid models, highlighting their role in enhancing grid reliability. The findings suggest that a hybrid approach integrating multiple ML models and sensor networks can significantly improve predictive accuracy while reducing operational risks.
Keywords: Machine Learning, Load Forecasting, Fault Prediction, Power Grid, Predictive Maintenance, Artificial Intelligence.
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