发表机构
Arizona State University; GE Vernova（亚利桑那州立大学; 通用电气维诺瓦）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对不可观测主配电系统拓扑不确定性问题，提出集成深度学习框架，结合相关性驱动传感器布置、双DNN状态估计与迁移学习，实现实时拓扑识别与状态估计，并在噪声下优于传统方法。
AI 中文摘要
分布式能源资源的快速整合正在从根本上改变主配电网络的潮流模式，并加剧运行不确定性。这些问题因缺乏实时态势感知和频繁的拓扑变化而进一步复杂化。为解决这些问题，本文提出了一种集成深度学习框架，用于在配备最少同步测量设备（SMD）的实时不可观测主配电网络中同时进行拓扑识别（TI）和配电系统状态估计（DSSE）。首先引入一种基于相关性的SMD布置算法，通过利用节点电压测量中的时间和空间相关性，同时满足TI精度和DSSE性能要求。其次开发了一种基于双深度神经网络（DNN）的DSSE模型，用于在各种运行条件下估计三相电压幅值和相角。为将框架扩展到基础拓扑之外，采用基于微调的迁移学习，利用有限的再训练数据将DSSE模型适应于重构后的拓扑。该框架在高斯和非高斯测量噪声下均得到验证，并与传统估计方法和单一DNN模型进行了基准比较。
英文摘要
The rapid integration of distributed energy resources is fundamentally altering power flow patterns in primary distribution networks and intensifying operational uncertainty. These problems are further compounded by lack of real-time situational awareness and frequent topology changes. To address these problems, this paper proposes an integrated deep learning framework for simultaneous topology identification (TI) and distribution system state estimation (DSSE) in real-time unobservable primary distribution networks instrumented by a minimal set of synchronized measurement devices (SMDs). A correlation-driven SMD placement algorithm is introduced first that jointly satisfies TI accuracy and DSSE performance requirements by exploiting temporal and spatial correlations in nodal voltage measurements. A dual deep neural network (DNN)-based DSSE model is developed next to estimate three-phase voltage magnitudes and angles across diverse operating conditions. To extend the framework beyond the base topology, fine-tuning-based transfer learning is employed to adapt the DSSE model to reconfigured topologies using limited retraining data. The framework is validated under both Gaussian and non-Gaussian measurement noise and benchmarked against a conventional estimation approach and a single DNN model.
Comments6 pages, 6 figures, 4 tables. Accepted for presentation at the 2026 North American Power Symposium (NAPS)
