论文标题
时间同步的状态估计对未完全观察到的分布系统使用深度学习考虑了逼真的测量噪声
Time Synchronized State Estimation for Incompletely Observed Distribution Systems Using Deep Learning Considering Realistic Measurement Noise
论文作者
论文摘要
由于实时可观察性有限，时间同步状态估计是分配系统的挑战。本文通过制定基于深度学习（DL）的方法来解决这一挑战，以执行不平衡的三相分配系统状态估计（DSSE）。最初，提供了一种以数据为驱动的方法，用于促进可靠的状态估计。然后，训练了深层神经网络（DNN），以执行DSSE的DSSE，用于通过同步测量器测量设备（SMD）观察到的系统。通过考虑对SMD的现实测量误差模型来证明所提出方法的鲁棒性。对基于经典线性状态估计的基于DNN的DSSE的比较研究表明，基于DL的方法可提供更好的准确性，而SMD数量明显较小。
Time-synchronized state estimation is a challenge for distribution systems because of limited real-time observability. This paper addresses this challenge by formulating a deep learning (DL)-based approach to perform unbalanced three-phase distribution system state estimation (DSSE). Initially, a data-driven approach for judicious measurement selection to facilitate reliable state estimation is provided. Then, a deep neural network (DNN) is trained to perform DSSE for systems that are incompletely observed by synchrophasor measurement devices (SMDs). Robustness of the proposed methodology is demonstrated by considering realistic measurement error models for SMDs. A comparative study of the DNN-based DSSE with classical linear state estimation indicates that the DL-based approach gives better accuracy with a significantly smaller number of SMDs.
