Fault Detection of Transmission Lines Using Unmanned Aerial Vehicle (UAV)
Abstract
The power transmission system is an essential part of the modern infrastructure that enables the distribution of electricity from power generation plants to households and industries. Transmission lines are the backbone of the power transmission system, and their proper functioning is crucial for the uninterrupted supply of electricity. However, faults in transmission lines are a common occurrence, and their timely detection and repair are crucial to ensure the reliability and efficiency of the power system. Traditional methods of detecting faults in transmission lines are labor-intensive, time-consuming, and expensive. Hence, there is a need for an efficient and cost effective solution to detect faults in transmission lines.The system "Fault Detection of Transmission Line Using UAV" proposes a novel approach that leverages the potential of unmanned aerial vehicles (UAVs) to detect faults in transmission lines. The proposed system involves equipping a UAV with high resolution cameras and sensors to capture images and data of the transmission lines. The UAV flies over the transmission lines and captures images and data of the entire transmission line, including the insulators, towers, and conductors. The images and data captured by the UAV are then processed using computer vision and machine learning algorithms to detect any abnormalities or faults in the transmission lines. The proposed system has several advantages over traditional methods of
detecting faults in transmission lines. First, the use of UAVs eliminates the need for human intervention, making the process faster, safer, and less costly. Second, the high-resolution images and data captured by the UAV provide a more comprehensive view of the transmission lines, enabling the detection of even minor abnormalities or faults that may be missed by traditional methods. Third, the use of computer vision and machine learning algorithms makes the fault detection process more accurate and efficient, reducing the risk of false alarms and minimizing the time required for repair. The proposed system can be scaled up to cover a larger area, enabling the detection of faults in a timely and accurate manner, thereby reducing downtime and maintenance costs for transmission lines. Furthermore, the proposed system can also be used for preventive maintenance, identifying potential faults before they occur, and reducing the risk of unexpected downtime. Overall, the proposed system "Fault Detection of Transmission Line Using UAV" presents an innovative solution to a critical problem in the power transmission system, potentially making it more reliable, efficient, and sustainable.
Keywords:
Autonomous UAV, computer vision, FPGA, hardware acceleration, mm Wave radar, Power line, sensor Fusion, AI, CNN, Automatic Fault Detection, PowerlinesPublished
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Jyothis Joseph, Angeetha Raju, Aparna Santhosh, Ashitha Jenish, K S Minu, Survey on Fake Profile Detection in Social Media , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Manjima M A, Soumya Anand, Partial Replacement of bitumen by Plant Polymer Lignin in Bituminous Pavement , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Nikita Niteen , Juby Mathew, Securing AI: Understanding and Defending Against Adversarial Attacks in Deep Learning Systems , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- P Sathya Narayan, Safad Ismail, Developing an Empathetic Interaction Model for Elderly in Pandemics , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Akhil Shaji, Albin Joshy, M J Athulkrishna, Joel Biju, Bino Thomas, COLLEGE BUS SECURITY AND MANAGEMENT SYSTEM , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Anna Jose, Anit Devesiya, Albin Scaria Sabu, Anand Baby John, Prof.Maria Yesudas, AMIGO APPLICATION , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Tebin Joseph, Pranav Thamban Nair, Sam Kattiveettil James, Mrs Tintu Alphonsa Thomas , Pest Prediction in Rice using IoT and Feed Forward Neural Network , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Jyothika Anil, Milan Joseph Mathew, Namitha S Mukkadan, Reshmi Raveendran, Rintu Jose, Driver Drowsiness Detection Using Smartphone Application , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Manna Mariam Abraham, Naveen Moncy Mathew , Richu Sakeer Hussain, Tima Jose Thachara , Bibin Varghese, Wild Watch Sentry , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Aiman Shahul, L Pavithra, Eldhose KV, S Thasni, Dany Jennez, S Resmara, Sand Battery Technology: A Promising Solution for Renewable Energy Storage , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
You may also start an advanced similarity search for this article.