Situational awareness (SA) is critical to properly operating active power distribution systems during normal and outage conditions. Appropriate SA tools should provide an accurate estimate of system voltage and current variables, the operational network topology, and the power injections from distributed energy sources (DERs), including behind-the-meter (BTM) photovoltaics (PVs). Obtaining an accurate SA, especially by estimating network topology and gross load demand, is increasingly challenging with the proliferation of BTM DERs with intermittent power generation. Moreover, the SA required for distribution system restoration is even more challenging after a medium to a prolonged outage due to inaccurate estimates of cold load pickup (CLPU) and switch statuses. This paper proposes an integrated real-time model update (RTMU) module for SA enhancement to help distribution system operators (DSOs) understand the power system conditions in dynamic and DER-rich environments. The proposed RTMU consists of several modules to obtain the required level of SA for operational decision-making. It includes estimators for a) BTM PV power, b) network topology, and c) CLPU. The proposed approaches leverage multiple data resources, deep learning approaches, and domain knowledge of the power system to provide the required level of SA to DSOs. The dependencies among these modules are actively leveraged to enhance SA under normal conditions and during power outages. We demonstrate and analyze the performance of the proposed RTMU on a modified IEEE 123-node feeder and a utility distribution system from the Western United States.

中文翻译：

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增强 DER-Rich 配电系统在停电前和停电后的态势感知

态势感知 (SA) 对于在正常和停电条件下正确操作有源配电系统至关重要。适当的 SA 工具应提供对系统电压和电流变量、运行网络拓扑以及分布式能源 (DER) 功率注入的准确估计，包括用户侧 (BTM) 光伏 (PV)。随着间歇发电的 BTM DER 的激增，获得准确的 SA，尤其是通过估计网络拓扑和总负载需求，变得越来越具有挑战性。此外，由于对冷负荷启动 (CLPU) 和开关状态的估计不准确，在中等到长时间停电后，配电系统恢复所需的 SA 更具挑战性。本文提出了一个用于增强 SA 的集成实时模型更新 (RTMU) 模块，以帮助配电系统运营商 (DSO) 了解动态和 DER 丰富环境中的电力系统状况。拟议的 RTMU 由几个模块组成，以获得运营决策所需的 SA 级别。它包括 a) BTM 光伏功率、b) 网络拓扑和 c) CLPU 的估算器。所提出的方法利用多种数据资源、深度学习方法和电力系统的领域知识来为 DSO 提供所需级别的 SA。这些模块之间的依赖关系被积极利用，以在正常情况下和停电期间增强 SA。