Graph Learning based Generative Design for Resilience of Interdependent Network Systems
This work addresses resilience design for interconnected systems like power networks, offering an incremental improvement through automated generative methods.
The study tackled the challenge of designing resilient interdependent network systems under disruptions by proposing a generative design method using graph learning algorithms, which efficiently generated new designs meeting performance criteria and demonstrated applicability on IEEE power system datasets.
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.