基于深度神经网络的不可观测配电网状态和拓扑估计
State and Topology Estimation for Unobservable Distribution Systems using Deep Neural Networks
摘要：
可重构配电网的时间同步状态估计由于实时可观测性有限而具有挑战性。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。两个深度神经网络（DNN）的训练时间同步的DNN为基础的TI和DSSE，分别为不完全观察的同步相量测量设备（SMD）的实时系统。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。
英文摘要：
Time-synchronized state estimation for reconfigurable distribution networks is challenging because of limited real-time observability. This paper 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 for time-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.
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