Making Smart Grids Smarter by Using Machine Learning

摘要

The huge amounts of data required by Smart grids operation are impossible to be processed by human operators in a timely manner. New intelligent systems should provide a clear decision regarding the system state. This paper proposes a new methodology based on supervised learning using AdaBoost and CARTs as decision support system for power system state classification. The methodology proves to be time efficient and precise, with low false negative rates. This approach could help in Smart Grids design and deployment, as it could be easily integrated into the existing EMS/SCADA systems.