TY - GEN
T1 - Active Fault Management for Networked Microgrids
AU - Wan, Wenfeng
AU - Li, Yan
AU - Yan, Bing
AU - Bragin, Mikhail A.
AU - Philhower, Jason
AU - Zhang, Peng
AU - Luh, Peter B.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - This paper presents Active Fault Management for Networked Microgrids (AFM-NM) to manage microgrids during faults. Two challenges remain in NMs' fault management. The first one is a lack of panoramic management schemes that allow easy change of objectives and constraints as needed rather than fixed on certain objectives and constraints. The second challenge is the implementation of distributed management for NWs under fault conditions instead of normal conditions. The two challenges are addressed in the presented AFM-NM. First, AFM-NM is formulated as an online optimization problem so that customized objectives and constraints can be conveniently added to the formulation, e.g., power ripples and balance, and fault current levels and contribution. Second, the coordination of each microgrid' AFM is enabled by the Surrogate Lagrangian Relaxation (SLR) method to allow distributed computation with guaranteed convergence. Comparison results with a conventional ride-through method show AFM-NM achieves desirable tradeoffs between different objectives. Comparison with centralized AFM demonstrates distributed AFM-NM ensures convergence and accuracy.
AB - This paper presents Active Fault Management for Networked Microgrids (AFM-NM) to manage microgrids during faults. Two challenges remain in NMs' fault management. The first one is a lack of panoramic management schemes that allow easy change of objectives and constraints as needed rather than fixed on certain objectives and constraints. The second challenge is the implementation of distributed management for NWs under fault conditions instead of normal conditions. The two challenges are addressed in the presented AFM-NM. First, AFM-NM is formulated as an online optimization problem so that customized objectives and constraints can be conveniently added to the formulation, e.g., power ripples and balance, and fault current levels and contribution. Second, the coordination of each microgrid' AFM is enabled by the Surrogate Lagrangian Relaxation (SLR) method to allow distributed computation with guaranteed convergence. Comparison results with a conventional ride-through method show AFM-NM achieves desirable tradeoffs between different objectives. Comparison with centralized AFM demonstrates distributed AFM-NM ensures convergence and accuracy.
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U2 - 10.1109/PESGM40551.2019.8973843
DO - 10.1109/PESGM40551.2019.8973843
M3 - Conference contribution
AN - SCOPUS:85079066906
T3 - IEEE Power and Energy Society General Meeting
BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PB - IEEE Computer Society
T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Y2 - 4 August 2019 through 8 August 2019
ER -