Collaborative Research: Lifelong Human-in-the-Loop Multiagent Learning for Decentralized Restoration of Distribution Systems (LifeGuard)

本研究开发一种新型的多智能体系统，提供一个实时的解决方案，配电服务恢复，提高能源供应弹性。多智能体框架将1）利用从图结构的电力系统动态数据中获得的时空信息来提供准确的故障识别和定位：2）提供一个分布式多智能体学习框架来寻找最优的恢复策略以快速恢复系统，同时考虑所生成的解决方案的可扩展性和时间效率; 3）通过有效地将它们的知识从源问题转移到其中电力网络的拓扑和特性改变的目标问题，获得足够灵活以适应新的恢复问题的终身电力恢复方案;以及4）为人类专家提供可解释的知识库，以基于他们的先验知识和专业知识来评估恢复方案并对其进行修改。学术界和教育界将受益于该项目产生的知识的快速传播。该研究计划鼓励研究生和本科生之间的创造性合作，以找到新的功能解决方案，以解决当前的配电网络运营挑战。该项目将为研究生和本科生开发新的课程，促进跨学科研究，并开展K-12外联活动。本研究的目的是开发一个分散的时空人工智能框架，分布式故障检测和识别，并在大型配电网络中的电力系统恢复考虑高维，稀疏，部分可观测的系统测量。本计画发展一个图胶囊网路，以辨识配电系统的时空动态模式，并侦测故障的类型与位置。此外，我们设计了一个新的完全分散的多智能体系统与演员-评论家强化学习，以解决大规模的恢复问题，高维系统的状态和行动。此外，我们将多智能体系统的知识转移作为机器学习中的一个开放性问题，并开发了一个能够适应电力系统拓扑结构和特性变化的终身恢复框架。该项目还将注意力模型纳入深度强化学习，为拟议的恢复框架开发可解释的知识库，可用于使用人类专业知识进行恢复知识验证和修改。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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. 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.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.