NSF Award
2223628
This research develops a novel multiagent system to provide a real-time solution for distribution service restoration that enhances energy supply resilience. The proposed multiagent framework will 1) leverage the spatiotemporal information obtained from the graph-structured power system dynamic data to provide accurate fault identification and location; 2) provide a distributed multiagent learning framework to find optimal restoration policies for rapid system recovery considering the scalability and time efficiency of the generated solutions; 3) obtain lifelong power restoration schemes that are flexible enough to adapt to new restoration problems by efficiently transferring their knowledge from a source problem to a target problem where the topology and characteristics of the power network are changed; and 4) provide an interpretable knowledge base for the human experts to evaluate the restoration scheme and modify it based on their prior knowledge and expertise. The academic and educational communities will benefit from the rapid dissemination of the generated knowledge from this project. The research plan encourages inventive collaboration among graduate and undergraduate students to find novel functional solutions to address current challenges in the distribution network operation. The project will develop a new curriculum for graduate and undergraduate students, promote interdisciplinary research, and develop K-12 outreach activities. <br/><br/>The objective of this research is to develop a decentralized spatiotemporal artificial intelligence framework for distributed fault detection and identification, and power system restoration in large-scale distribution power networks considering the high dimensionality, sparsity, and partial observability of the system measurements. The project develops a graph capsule network to recognize spatiotemporal dynamic patterns of distribution systems, and detect the type and location of faults. Moreover, we devise a novel fully decentralized multiagent system with actor-critic reinforcement learning to solve large-scale restoration problems with high-dimensional system states and actions. Furthermore, we address knowledge transfer of multiagent systems as an open problem in machine learning and develop a lifelong restoration framework capable of adapting to changes in the topology and characteristics of the power system. The project also incorporates attention models into deep reinforcement learning to develop an interpretable knowledge base for the proposed restoration framework that can be used for restoration knowledge verification and modification using human expertise.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
