Machine Learning Based Energy Management System for Improvement of Critical Reliability




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Current power distribution system faces challenges that originate from aging infrastructure, DER integration, and frequent natural disasters. At the same time, the contingencies are one of the leading causes of economic loss and loss of life during natural disasters. Hence, improvement of reliability of power to critical loads during such emergency scenarios is one of the key features to be addressed during the development of a future power distribution system. An ideal solution will include a retro-fit device that can utilize existing infrastructure and integrate DERs advantageously. One of such solutions has been visualized using Advanced Micro Grid systems. The presented study utilizes Machine learning algorithms to create energy management systems for such micro-grids with the goal of maximizing power availability to the critical loads of the community. The residential level electronics with integrated artificial intelligence have been designed, developed, and tested as a part of this effort.



Power electronics, Electric power systems, Microgrids (Smart power grids), Machine learning, Reliability (Engineering)


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