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

共 50 条

[41] Deep Neural Network Approach in Robot Tool Dynamics Identification for Bilateral Teleoperation
Su, Hang
Qi, Wen
Yang, Chenguang
Sandoval, Juan
Ferrigno, Giancarlo
De Momi, Elene
IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5 (02): : 2943 - 2949
[42] Online heat flux estimation using artificial neural network as a digital filter approach
Najafi, Hamidreza
Woodbury, Keith A.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2015, 91 : 808 - 817
[43] Compressing Deep Neural Network: A Black-Box System Identification Approach
Sahu, Ishan
Pal, Arpan
Ukil, Arijit
Majumdar, Angshul
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
[44] Estimation of the ends pressure drop in an online capillary rheometer - Using the neural network approach
Chiu, SH
Pong, SH
JOURNAL OF APPLIED POLYMER SCIENCE, 1999, 73 (11) : 2183 - 2186
[45] Distribution Network Electrical Topology Identification Algorithm Based on Neural Network
Liu L.
Wang T.
Zhou Y.
Cheng Z.
Li F.
Zhang Y.
Xu J.
Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2023, 52 (02): : 247 - 253
[46] Deep-Learning-Based Neural Network Training for State Estimation Enhancement: Application to Attitude Estimation
Al-Sharman, Mohammad K.
Zweiri, Yahya
Jaradat, Mohammad Abdel Kareem
Al-Husari, Raghad
Gan, Dongming
Seneviratne, Lakmal D.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2020, 69 (01) : 24 - 34
[47] ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum
Pant, Gaurav
Yadav, D. P.
Gaur, Ashish
ALGAL RESEARCH-BIOMASS BIOFUELS AND BIOPRODUCTS, 2020, 48
[48] Quantum topology identification with deep neural networks and quantum walks
Yurui Ming
Chin-Teng Lin
Stephen D. Bartlett
Wei-Wei Zhang
npj Computational Materials, 5
[49] Quantum topology identification with deep neural networks and quantum walks
Ming, Yurui
Lin, Chin-Teng
Bartlett, Stephen D.
Zhang, Wei-Wei
NPJ COMPUTATIONAL MATERIALS, 2019, 5 (1)
[50] Deep neural network based distribution system state estimation using hyperparameter optimization
Bekesi, Gergo
Barancsuk, Lilla
Hartmann, Balint
RESULTS IN ENGINEERING, 2024, 24

← 1 2 3 4 5 →