Abstract
The event of electricity outage could cause huge financial losses for the industry and inconvenience to the consumers. Vegetation encroachment at the power transmission right-of-way is one of the main causes. Transmission line fault could occur when a tree falls into the vicinity of power transmission line. Conventional inspection method such as ground inspection is the simplest approach to counter vegetation encroachment. However, technical personnel is required to travel on site to perform the inspection manually. This process is often time consuming and prone to human error. Airborne Light Detection and Ranging (LiDAR) system and satellite imagery are remote sensing approaches to inspect the power transmission right-of-way. These approaches could reduce reliance on physical site inspection and remove human error. However, large dataset needs to be processed and specialist equipment is needed for this method which also increases the overall cost. In this research, a simple yet cost effective method is used to detect vegetation encroachment by using near aerial infrared (NIR) image processing approach. The process is divided into two parts. First, detect the inconspicuous power transmission line by utilizing Radon Transform (RT) in vertical derivative image and detect the peaks of the Radon Transform. Next, detect the vegetation encroachment in the clearance zone by using green normalized difference vegetation index (GNDVI) algorithm to differentiate between trees and glassy plains. Preliminary experiment results show a satisfactory performance in detecting vegetation encroachment at the power transmission right-of-way.
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Acknowledgements
Kementerian Pengajian Tinggi Malaysia, Fundamental Research Grant Scheme (FRGS), FRGS/1/2020/TK0/UNIMAS/02/14.
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Lim, P.Y., Bong, D., Ting, K.C., Lothai, F.F., Joseph, A., Zulcaffle, T.M. (2024). Near Infrared Remote Sensing of Vegetation Encroachment at Power Transmission Right-of-Way. In: Ahmad, N.S., Mohamad-Saleh, J., Teh, J. (eds) Proceedings of the 12th International Conference on Robotics, Vision, Signal Processing and Power Applications. RoViSP 2021. Lecture Notes in Electrical Engineering, vol 1123. Springer, Singapore. https://doi.org/10.1007/978-981-99-9005-4_64
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DOI: https://doi.org/10.1007/978-981-99-9005-4_64
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