Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models
Adaptive protection schemes (APS) have gained prominence in maintaining the integrity of overcurrent relay (OCR) settings in reconfigurable networks. While many APSs rely on supervisory control and data acquisition systems, they are very expensive and expose the system to vulnerabilities arising fro...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Article |
Published: |
Institute of Electrical and Electronics Engineers Inc.
2024
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/44842/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.44842 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.448422024-07-01T01:48:42Z http://eprints.um.edu.my/44842/ Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models Wong, Junying Tan, Chia Kwang Abd Rahim, Nasrudin Tan, Rodney H.G. TK Electrical engineering. Electronics Nuclear engineering Adaptive protection schemes (APS) have gained prominence in maintaining the integrity of overcurrent relay (OCR) settings in reconfigurable networks. While many APSs rely on supervisory control and data acquisition systems, they are very expensive and expose the system to vulnerabilities arising from communication failures. Recent studies have proposed communication-less APSs to address this issue by relying on data-mining algorithms equipped with real-time fault voltage-current information. However, the OCR settings are computed and updated as the fault occurs, inevitably causing prolonged OCR tripping in these schemes. This contradicts with the APS' original purpose of minimizing OCR operation time and consequent equipment damage. Thus, a load flow-based APS that addresses this flaw is proposed to achieve primary-backup OCR coordination in a highly reconfigurable system. Network topologies are first categorized into OCR setting groups via clustering analysis. A nonparametric probability model is developed to evaluate the probability of network topologies at a measured load flow. Then, a machine learning model deployed in a local controller selects the correct setting groups based on the calculated probabilities. The proposed APS achieves high accuracies and low OCR operating times in the IEEE 33-bus test distribution system under varying load conditions and network topologies. © 1986-2012 IEEE. Institute of Electrical and Electronics Engineers Inc. 2024 Article PeerReviewed Wong, Junying and Tan, Chia Kwang and Abd Rahim, Nasrudin and Tan, Rodney H.G. (2024) Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models. IEEE Transactions on Power Delivery, 39 (1). 202 – 209. ISSN 0885-8977, DOI https://doi.org/10.1109/TPWRD.2023.3330730 <https://doi.org/10.1109/TPWRD.2023.3330730>. 10.1109/TPWRD.2023.3330730 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Wong, Junying Tan, Chia Kwang Abd Rahim, Nasrudin Tan, Rodney H.G. Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
description |
Adaptive protection schemes (APS) have gained prominence in maintaining the integrity of overcurrent relay (OCR) settings in reconfigurable networks. While many APSs rely on supervisory control and data acquisition systems, they are very expensive and expose the system to vulnerabilities arising from communication failures. Recent studies have proposed communication-less APSs to address this issue by relying on data-mining algorithms equipped with real-time fault voltage-current information. However, the OCR settings are computed and updated as the fault occurs, inevitably causing prolonged OCR tripping in these schemes. This contradicts with the APS' original purpose of minimizing OCR operation time and consequent equipment damage. Thus, a load flow-based APS that addresses this flaw is proposed to achieve primary-backup OCR coordination in a highly reconfigurable system. Network topologies are first categorized into OCR setting groups via clustering analysis. A nonparametric probability model is developed to evaluate the probability of network topologies at a measured load flow. Then, a machine learning model deployed in a local controller selects the correct setting groups based on the calculated probabilities. The proposed APS achieves high accuracies and low OCR operating times in the IEEE 33-bus test distribution system under varying load conditions and network topologies. © 1986-2012 IEEE. |
format |
Article |
author |
Wong, Junying Tan, Chia Kwang Abd Rahim, Nasrudin Tan, Rodney H.G. |
author_facet |
Wong, Junying Tan, Chia Kwang Abd Rahim, Nasrudin Tan, Rodney H.G. |
author_sort |
Wong, Junying |
title |
Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
title_short |
Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
title_full |
Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
title_fullStr |
Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
title_full_unstemmed |
Communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
title_sort |
communication-less adaptive overcurrent protection for highly reconfigurable systems based on nonparametric load flow models |
publisher |
Institute of Electrical and Electronics Engineers Inc. |
publishDate |
2024 |
url |
http://eprints.um.edu.my/44842/ |
_version_ |
1805881176086282240 |
score |
13.211869 |