The use of artificial neural network for low latency of fault detection and localisation in transmission line
One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults dete...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023005832 |
| _version_ | 1811161791015682048 |
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| author | Vincent Nsed Ogar Sajjad Hussain Kelum A.A. Gamage |
| author_facet | Vincent Nsed Ogar Sajjad Hussain Kelum A.A. Gamage |
| author_sort | Vincent Nsed Ogar |
| collection | DOAJ |
| description | One of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 μs of detection and an average error of 0%–0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system. |
| first_indexed | 2024-04-10T06:20:01Z |
| format | Article |
| id | doaj.art-e4e405bf4d004caab65fb5e808da42b4 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-10T06:20:01Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-e4e405bf4d004caab65fb5e808da42b42023-03-02T05:01:17ZengElsevierHeliyon2405-84402023-02-0192e13376The use of artificial neural network for low latency of fault detection and localisation in transmission lineVincent Nsed Ogar0Sajjad Hussain1Kelum A.A. Gamage2Corresponding author.; Department of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United KingdomDepartment of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United KingdomDepartment of Electrical and Electronic Engineering, James Watt School of Engineering, University of Glasgow, Glasgow, United KingdomOne of the most critical concerns in power system reliability is the timely and accurate detection of transmission line faults. Therefore, accurate detection and localisation of these faults are necessary to avert system collapse. This paper focuses on using Artificial Neural Networks in faults detection and localisation to attain accuracy, precision and speed of execution. A 330 kV, 500 km three-phase transmission line was modelled to extract faulty current and voltage data from the line. The Artificial Neural Network technique was used to train this data, and an accuracy of 100% was attained for fault detection and about 99.5% for fault localisation at different distances with 0.0017 μs of detection and an average error of 0%–0.5%. This model performs better than Support Vector Machine and Principal Component Analysis with a higher fault detection time. This proposed model serves as the basis for transmission line fault protection and management system.http://www.sciencedirect.com/science/article/pii/S2405844023005832Transmission lineFault protectionMachine learningFault detectionFault localisation |
| spellingShingle | Vincent Nsed Ogar Sajjad Hussain Kelum A.A. Gamage The use of artificial neural network for low latency of fault detection and localisation in transmission line Heliyon Transmission line Fault protection Machine learning Fault detection Fault localisation |
| title | The use of artificial neural network for low latency of fault detection and localisation in transmission line |
| title_full | The use of artificial neural network for low latency of fault detection and localisation in transmission line |
| title_fullStr | The use of artificial neural network for low latency of fault detection and localisation in transmission line |
| title_full_unstemmed | The use of artificial neural network for low latency of fault detection and localisation in transmission line |
| title_short | The use of artificial neural network for low latency of fault detection and localisation in transmission line |
| title_sort | use of artificial neural network for low latency of fault detection and localisation in transmission line |
| topic | Transmission line Fault protection Machine learning Fault detection Fault localisation |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023005832 |
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