UAV Inspections of Power Transmission Networks with AI Technology: A Case Study of Lesvos Island in Greece

Georgios Chatzargyros (), Apostolos Papakonstantinou, Vasiliki Kotoula, Dimitrios Stimoniaris and Dimitrios Tsiamitros
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Georgios Chatzargyros: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Apostolos Papakonstantinou: SciDrones, P.O. Box 94 ELTA C.O., 81100 Mytilene, Greece
Vasiliki Kotoula: Renel I.K.E, 26th October 90 & Minotavrou 1st, 54627 Thessaloniki, Greece
Dimitrios Stimoniaris: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece
Dimitrios Tsiamitros: Department of Electrical and Computer Engineering, University of Western Macedonia, 50100 Kozani, Greece

Energies, 2024, vol. 17, issue 14, 1-17

Abstract: The inspection of overhead power transmission lines is of the utmost importance to ensure the power network’s uninterrupted, safe, and reliable operation. The increased demand for frequent inspections implementing efficient and cost-effective methods has emerged, since conventional manual inspections are highly inaccurate, time-consuming, and costly and have geographical and weather restrictions. Unmanned Aerial Vehicles are a promising solution for managing automatic inspections of power transmission networks. The project “ALTITUDE (Automatic Aerial Network Inspection using Drones and Machine Learning)” has been developed to automatically inspect the power transmission network of Lesvos Island in Greece. The project combines drones, 5G data transmission, and state-of-the-art machine learning algorithms to replicate the power transmission inspection process using high-resolution UAV data. This paper introduces the ALTITUDE platform, created within the frame of the ALTITUDE project. The platform is a web-based, responsive Geographic Information System (GIS) that allows registered users to upload bespoke drone imagery of medium-voltage structures fed into a deep learning algorithm for detecting defects, which can be either exported as report spreadsheets or viewed on a map. Multiple experiments have been carried out to train artificial intelligence (AI) algorithms to detect faults automatically.

Keywords: drones; power line inspection; machine learning; overhead power line; artificial intelligence; inspection platform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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