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

Journal Article · · IET Generation, Transmission, & Distribution
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  1. Energy Delivery and Utilization Group Computational Engineering Division Lawrence Livermore National Laboratory Livermore CA United States
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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.

Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE; USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1759122
Report Number(s):
LLNL-JRNL-754921
Journal Information:
IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Journal Issue: 14 Vol. 13; ISSN 1751-8687
Publisher:
Institution of Engineering and Technology (IET)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

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