A Novel Transfer Learning Approach to Detect the Location of Transformers in Distribution Network

Muhammad Ali; Carlos A Macana; Krishneel Prakash; Bill Tarlinton; Robi Islam; Hemanshu R Pota

doi:10.1109/icSmartGrid49881.2020.9144863

Back

A Novel Transfer Learning Approach to Detect the Location of Transformers in Distribution Network

Conference paper

Peer reviewed

A Novel Transfer Learning Approach to Detect the Location of Transformers in Distribution Network

Muhammad Ali, Carlos A Macana, Krishneel Prakash, Bill Tarlinton, Robi Islam and Hemanshu R Pota

2020 8th International Conference on Smart Grid (icSmartGrid), pp.56-60

International Conference on Smart Grid (icSmartGrid), 8th (Paris, France, 17-Jun-2020–19-Jun-2020)

Institute of Electrical and Electronics Engineers

2020

DOI: https://doi.org/10.1109/icSmartGrid49881.2020.9144863

Files and links (1)

url

https://dx.doi.org/10.1109/icSmartGrid49881.2020.9144863View

Published Version

Abstract

designing distribution networks

transfer learning

distribution transformers

artificial intelligence

One of the key requirements for designing reliable and scalable electrical distribution networks is the identification of the correct location of its electrical components. The location of transformers in the distribution network is crucial as they are the backbone for transforming one form of electricity into another. In this study, a transfer learning approach based on artificial intelligence is presented to determine the location of transformers in distribution networks. To implement the concept, a dataset of transformer images is collected from public platforms such as Google images. The collected dataset is then used to train the transfer learning model developed in Python. A custom object detection model for transformers is developed using the YOLOv3 architecture. To evaluate the potential of proposed approach, the developed model is tested on real-world images of transformers. The experimental results show that the transfer learning method can achieve a high detection accuracy for the detection of transformer locations. The presented research would be useful in the educational and practical fields as it could be used by electricity companies to develop, extend, and prepare modern distribution networks.

Details

Title: A Novel Transfer Learning Approach to Detect the Location of Transformers in Distribution Network
Authors: Muhammad Ali (Author) - UNSW Australia
Carlos A Macana (Author) - Zeppelin Bend (Australia)
Krishneel Prakash (Author) - UNSW Australia
Bill Tarlinton (Author) - Zeppelin Bend (Australia)
Robi Islam (Author) - University of the Sunshine Coast, Queensland, School of Science and Engineering - Legacy
Hemanshu R Pota (Author) - UNSW Australia
Publication details: 2020 8th International Conference on Smart Grid (icSmartGrid), pp.56-60
Conference details: International Conference on Smart Grid (icSmartGrid), 8th (Paris, France, 17-Jun-2020–19-Jun-2020)
Publisher: Institute of Electrical and Electronics Engineers
Date published: 2020
DOI: 10.1109/icSmartGrid49881.2020.9144863; 10.1109/icSmartGrid49881.2020
ISBN: 9781728111070
Organisation Unit: School of Science and Engineering - Legacy; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99487908302621
Output Type: Conference paper

Metrics

85 Record Views