Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem....Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem.Here,federated learning is used to train each microgrid’s network with training data achieved from distributed optimization.The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm.The replacement transfers computation from online to offline.With this replacement,the control algorithm can meet real-time requirements for a system with dozens of microgrids.By contrast,distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids.More microgrids,however,lead to more computation time with optimization-based method.Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids.Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.展开更多
基金supported in part by the National Science Foundation under Grants No.OIA-2134840 and ECCS-1810108in part by Department of Energy under Grant No.DE-EE0009341Department of Navy award N00014-20-1-2858 issued by the Office of Naval Research.
文摘Federated-learning-based active fault management(AFM)is devised to achieve real-time safety assurance for microgrids and the main grid during faults.AFM was originally formulated as a distributed optimization problem.Here,federated learning is used to train each microgrid’s network with training data achieved from distributed optimization.The main contribution of this work is to replace the optimization-based AFM control algorithm with a learning-based AFM control algorithm.The replacement transfers computation from online to offline.With this replacement,the control algorithm can meet real-time requirements for a system with dozens of microgrids.By contrast,distributed-optimization-based fault management can output reference values fast enough for a system with several microgrids.More microgrids,however,lead to more computation time with optimization-based method.Distributed-optimization-based fault management would fail real-time requirements for a system with dozens of microgrids.Controller hardware-in-the-loop real-time simulations demonstrate that learning-based AFM can output reference values within 10 ms irrespective of the number of microgrids.
