Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning
复制标题
基于深度学习的不可观测配电系统贝叶斯状态估计
DOI：
10.1109/tpwrs.2019.2919157
复制
发表时间：
2018-11
影响因子：
6.6
通讯作者：
Kursat Rasim Mestav;Jaime Luengo-Rozas;L. Tong
中科院分区：
文献类型：
--
作者：
Kursat Rasim Mestav;Jaime Luengo-Rozas;L. Tong
研究了不可观测分布系统的状态估计问题。提出了一种用于实时应用的贝叶斯状态估计的深度学习方法。所提出的技术包括随机功率注入的分布学习，用于训练用于状态估计的深度神经网络的蒙特卡罗技术，以及贝叶斯坏数据检测和过滤算法。研究了深度神经网络的结构特征。仿真说明了贝叶斯状态估计对不可观测系统的准确性，并证明了采用深度神经网络的好处。数值结果表明，贝叶斯状态估计对建模和估计误差以及坏数据和缺失数据的存在具有鲁棒性。与伪测量技术相比，通过深度学习神经网络的直接贝叶斯状态估计优于现有的基准。
The problem of state estimation for unobservable distribution systems is considered. A deep learning approach to Bayesian state estimation is proposed for real-time applications. The proposed technique consists of distribution learning of stochastic power injection, a Monte Carlo technique for the training of a deep neural network for state estimation, and a Bayesian bad-data detection and filtering algorithm. Structural characteristics of the deep neural networks are investigated. Simulations illustrate the accuracy of Bayesian state estimation for unobservable systems and demonstrate the benefit of employing a deep neural network. Numerical results show the robustness of Bayesian state estimation against modeling and estimation errors and the presence of bad and missing data. Comparing with pseudo-measurement techniques, direct Bayesian state estimation via deep learning neural network outperforms existing benchmarks.