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

Duan, Nan; Stewart, Emma M.

doi:10.1049/iet-gtd.2018.6195

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

Journal Article · 17 June 2019 · IET Generation, Transmission, & Distribution

DOI: https://doi.org/10.1049/iet-gtd.2018.6195 · OSTI ID:1759122

^[1]; Stewart, Emma M. ^[1]

Energy Delivery and Utilization GroupComputational Engineering DivisionLawrence Livermore National LaboratoryLivermoreCAUnited States

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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1759122

Alternate ID(s):: OSTI ID: 1579623; OSTI ID: 1786772

Report Number(s):: LLNL-JRNL-754921

Journal Information:: IET Generation, Transmission, & Distribution, Journal Name: IET Generation, Transmission, & Distribution Vol. 13 Journal Issue: 14; ISSN 1751-8687

Publisher:: Institution of Engineering and Technology (IET)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Cited by: 13 works

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