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Title: Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources

Abstract

This study proposes a data-driven approach for identifying switch actions in power distribution networks. Simulated micro-phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi-phase multi-switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random-forest-based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123-node feeder modelled in GridLAB-D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.

Authors:
 [1];  [1]
  1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1579623
Report Number(s):
[LLNL-JRNL-754921]
[Journal ID: ISSN 1751-8687; 941279]
Grant/Contract Number:  
[AC52-07NA27344]
Resource Type:
Accepted Manuscript
Journal Name:
IET Generation, Transmission, & Distribution
Additional Journal Information:
[ Journal Volume: 13; Journal Issue: 14]; Journal ID: ISSN 1751-8687
Publisher:
Institution of Engineering and Technology
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING

Citation Formats

Duan, Nan, and Stewart, Emma M. Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources. United States: N. p., 2019. Web. doi:10.1049/iet-gtd.2018.6195.
Duan, Nan, & Stewart, Emma M. Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources. United States. doi:10.1049/iet-gtd.2018.6195.
Duan, Nan, and Stewart, Emma M. Thu . "Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources". United States. doi:10.1049/iet-gtd.2018.6195. https://www.osti.gov/servlets/purl/1579623.
@article{osti_1579623,
title = {Deep-learning-based power distribution network switch action identification leveraging dynamic features of distributed energy resources},
author = {Duan, Nan and Stewart, Emma M.},
abstractNote = {This study proposes a data-driven approach for identifying switch actions in power distribution networks. Simulated micro-phasor measurement unit data is utilised to train a convolutional neural network (CNN) model. The trained CNN model can identify multi-phase multi-switch actions. Instead of working as a blackbox, the proposed approach extracts the features from the hidden layers of the trained CNN for engineering interpretation and error check. In addition, a random-forest-based feature ranking algorithm is proposed to identify the most important features. The proposed approach is validated on the IEEE 123-node feeder modelled in GridLAB-D. The CNN model is built and trained using TensorFlow. The proposed approach achieves 96.57% identification accuracy.},
doi = {10.1049/iet-gtd.2018.6195},
journal = {IET Generation, Transmission, & Distribution},
number = [14],
volume = [13],
place = {United States},
year = {2019},
month = {7}
}

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