TY - GEN
T1 - GENERATIVE DESIGN FOR RESILIENCE OF INTERDEPENDENT NETWORK SYSTEMS
AU - Wu, Jiaxin
AU - Wang, Pingfeng
N1 - Funding Information:
This research is partially supported the U.S. Department of Energy’s Office of Nuclear Energy under Award No. DENE0008899 and the National Science Foundation (NSF) Engineering Research Center for Power Optimization of ElectroThermal Systems (POETS) with cooperative agreement EEC-1449548.
Publisher Copyright:
Copyright © 2022 by ASME.
PY - 2022
Y1 - 2022
N2 - Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. While the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on power systems from the IEEE dataset have illustrated the applicability of the proposed method for designing resilient interconnected systems.
AB - Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. While the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on power systems from the IEEE dataset have illustrated the applicability of the proposed method for designing resilient interconnected systems.
KW - generative design
KW - graph neural network
KW - network systems
KW - network systems
KW - power systems
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U2 - 10.1115/DETC2022-89622
DO - 10.1115/DETC2022-89622
M3 - Conference contribution
AN - SCOPUS:85142484990
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 48th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2022
Y2 - 14 August 2022 through 17 August 2022
ER -