论文标题
通过AI/ML授权的5G网络接近实时分布式状态估计
Near Real-Time Distributed State Estimation via AI/ML-Empowered 5G Networks
论文作者
论文摘要
第五代（5G）网络有可能加速电源系统过渡到灵活，软焊，数据驱动和智能网格。凭借对机器学习（ML）/人工智能（AI）功能的不断发展的支持，5G网络有望实现新颖的以数据为中心的智能电网（SG）服务。在本文中，我们探讨了如何在共生关系中与ML/AI-ai-ai-5G网络集成在一起的SG服务。我们专注于状态估计（SE）作为能源管理系统的关键要素，并专注于两个主要问题。首先，以教程的方式，我们介绍了如何将分布式SE与5G核心网络和无线电访问网络体系结构的元素集成在一起的概述。其次，我们介绍并比较了基于以下方面的两种强大的分布式SE方法：i）图形模型和信念传播以及ii）图形神经网络。我们讨论了他们的性能和能力，以考虑通信延迟，以通过5G网络支持接近实时的SE。
Fifth-Generation (5G) networks have a potential to accelerate power system transition to a flexible, softwarized, data-driven, and intelligent grid. With their evolving support for Machine Learning (ML)/Artificial Intelligence (AI) functions, 5G networks are expected to enable novel data-centric Smart Grid (SG) services. In this paper, we explore how data-driven SG services could be integrated with ML/AI-enabled 5G networks in a symbiotic relationship. We focus on the State Estimation (SE) function as a key element of the energy management system and focus on two main questions. Firstly, in a tutorial fashion, we present an overview on how distributed SE can be integrated with the elements of the 5G core network and radio access network architecture. Secondly, we present and compare two powerful distributed SE methods based on: i) graphical models and belief propagation, and ii) graph neural networks. We discuss their performance and capability to support a near real-time distributed SE via 5G network, taking into account communication delays.
