Open AccessDeep‐learning‐based power distribution network switch action identification leveraging dynamic features of distributed energy resources
Author(s) -
Duan Nan,
Stewart Emma M.
Publication year - 2019
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2018.6195
Subject(s) - computer science , artificial intelligence , identification (biology) , convolutional neural network , ranking (information retrieval) , phasor measurement unit , feature engineering , feature (linguistics) , phasor , random forest , smart grid , machine learning , data mining , deep learning , power (physics) , electric power system , engineering , biology , linguistics , philosophy , botany , physics , quantum mechanics , electrical engineering
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.