Applying AI to Power Line Inspection: Recent Developments

1 University of Tennessee at Chattanooga, Dep. of Eng. Management & Technology, Chattanooga, TN, USA.
2 Istanbul Technical University, Dep. of Electrical Engineering, Istanbul, Turkiye.
3 University of California Riverside, WCGEC, Riverside, CA, USA.

* Corresponding author. Tel.: +1-423-425-5840; email: gokhan-erdemir@utc.edu (G.E.)
Manuscript submitted May 10, 2023; accepted July 1, 2023; published November 14, 2023.

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Abstract—As the backbone of modern civilization, electricity fuels countless homes, businesses, and industries worldwide. However, this indispensable resource's safety, reliability, and efficiency hinge upon the regular maintenance and inspection of electric power lines. With the rise of AI technologies, there has been mounting interest in their application to power line inspection, with the overarching goal of augmenting the accuracy and efficiency of this process. This exhaustive review article surveys the latest research in the field of AI for power line inspection, with a specific emphasis on automated image recognition, predictive maintenance, robotic inspection, data analytics, and automated reporting. Through a meticulous literature analysis, we expose AI technologies' potential advantages and drawbacks in power line inspection and highlight the key challenges and opportunities for future research. Our sweeping review emphasizes the unprecedented potential of AI technologies in fundamentally transforming power line inspection with an array of inventive and pioneering approaches to enhancing the safety and reliability of this vital infrastructure.

Index Terms—Power line inspection, artificial intelligence, machine learning, predictive maintenance, robotic inspection, data analytics, image recognition, automated reporting

Cite: Gokhan Erdemir^1,*, Aydin Tarik Zengin², Tahir Cetin Akinci^2,3, "Applying AI to Power Line Inspection: Recent Developments," Journal of Advances in Artificial Intelligence vol. 1, no. 3, pp. 141-153, 2023.

Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).