A Deep Neural Network Approach for Online Topology Identification in State Estimation

被引：31

作者：

Gotti, Davide ^{[1
]}

Amaris, Hortensia ^{[1
]}

Ledesma, Pablo ^{[1
]}

机构：

[1] Univ Carlos III Madrid, Dept Elect Engn, Madrid 28911, Spain

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2021年 / 36卷 / 06期

关键词：

Network topology; Topology; State estimation; Training; Neurons; Switches; Measurement uncertainty; Topology identification; deep neural network; state estimation; bad data detection and identification;

D O I：

10.1109/TPWRS.2021.3076671

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces a network topology identification (TI) method based on deep neural networks (DNNs) for online applications. The proposed TI DNN utilizes the set of measurements used for state estimation to predict the actual network topology and offers low computational times along with high accuracy under a wide variety of testing scenarios. The training process of the TI DNN is duly discussed, and several deep learning heuristics that may be useful for similar implementations are provided. Simulations on the IEEE 14-bus and IEEE 39-bus test systems are reported to demonstrate the effectiveness and the small computational cost of the proposed methodology.

引用

页码：5824 / 5833

页数：10

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