State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks.
State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks.
复制标题
使用深度神经网络对不可观测的配电系统进行状态和拓扑估计。
DOI：
10.1109/tim.2022.3167722
复制
发表时间：
2022
影响因子：
5.6
通讯作者：
Dasarathy,Gautam
中科院分区：
文献类型：
--
作者：
Azimian,Behrouz;Biswas,ReetamSen;Moshtagh,Shiva;Pal,Anamitra;Tong,Lang;Dasarathy,Gautam
由于可重构配电网的实时可观测性有限，时间同步状态估计是一个具有挑战性的问题。本文通过制定基于深度学习（DL）的拓扑识别（TI）和不平衡三相配电系统状态估计（DSSE）方法来解决这一挑战。针对同步相量测量设备（SMD）真实的实时观测不完全的系统，分别训练了两个深度神经网络（DNN）用于时间同步的基于DNN的TI和DSSE。还提供了一种数据驱动的方法，用于明智的SMD放置，以促进可靠的TI和DSSE。通过考虑SMD测量中的非高斯噪声，证明了所提出的方法的鲁棒性。基于DNN的DSSE与更传统的方法的比较表明，基于DL的方法在SMD数量较少的情况下提供了更好的准确性。
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This article addresses this challenge by formulating a deep learning (DL)-based approach for topology identification (TI) and unbalanced three-phase distribution system state estimation (DSSE). Two deep neural networks (DNNs) are trained fortime-synchronized DNN-based TI and DSSE, respectively, for systems that are incompletely observed by synchrophasor measurement devices (SMDs) in real time. A data-driven approach for judicious SMD placement to facilitate reliable TI and DSSE is also provided. Robustness of the proposed methodology is demonstrated by considering non-Gaussian noise in the SMD measurements. A comparison of the DNN-based DSSE with more conventional approaches indicates that the DL-based approach gives better accuracy with smaller number of SMDs.