Deep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources. Issue 14 (17th June 2019)

• Record Type: Journal Article
• Authors: Duan, Nan; Stewart, Emma M.
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 14(2019)
• Page Start: 3139
• Page End: 3147
• Publication Date: 2019-06-17
• Subjects: distribution networks -- distributed power generation -- phasor measurement -- power engineering computing -- random forests -- convolutional neural nets; distributed energy resources -- data‐driven approach -- switch actions -- power distribution networks -- simulated microphasor measurement unit data -- convolutional neural network model -- trained CNN model -- multiswitch actions -- random‐forest‐based feature ranking algorithm -- deep‐learning‐based power distribution network switch action identification -- dynamic features; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6195
• Languages: English
• ISSNs: 1751-8687

Abstract

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.