Session: DAC-09-01-Design for Resilience and Failure Recovery

Paper Number: 89622

89622 - Generative Design for Resilience of Interdependent Network Systems 

Interconnected complex systems such as power networks usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by hash operating environments or regional nature diaster 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. In order to tackle these challenges in designing a resilient networked system efficiently, this study presents a generative design method that utilizes graph-based learning algorithms to better explore the design space. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing networked systems and therefore 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 on power systems from the IEEE test system datasets have been employed and case study results illustrated the applicability of the developed method for designing resilient interconnected systems.

Presenting Author: Jiaxin Wu University of Illinois at Urbana-Champaign

Presenting Author Biography: Mr. Jiaxin Wu is currently a PhD Candidate in the Department of Industrial and Enterprise Systems Engineering at University of Illinois at Urbana-Champaign.

Authors:

Jiaxin Wu University of Illinois at Urbana-Champaign
Pingfeng Wang University of Illinois At Urbana-Champaign