Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR
Dynamic Line Rating (DLR) is a process which electrical network operators can implement to improve efficiency by dynamically adjusting the load capacity as conditions allow. To implement DLR an accurate model of the conductors and their clearances is needed. Airborne LiDAR, while expensive, is the m...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/22/3718 |
| _version_ | 1797548059380416512 |
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| author | Josh McCulloch Richard Green |
| author_facet | Josh McCulloch Richard Green |
| author_sort | Josh McCulloch |
| collection | DOAJ |
| description | Dynamic Line Rating (DLR) is a process which electrical network operators can implement to improve efficiency by dynamically adjusting the load capacity as conditions allow. To implement DLR an accurate model of the conductors and their clearances is needed. Airborne LiDAR, while expensive, is the most common method of collecting line data as it is fast and is of high quality. State of the art methods for automatically reconstructing conductors first classify conductor points before fitting conductor models. This approach works well for high tension lines with significant separation between conductors but tends to perform poorly in urban environments where conductors are packed tightly together and surrounded by clutter. The method presented in this article attempts to overcome these challenges by performing an informed search for the conductors, anchored to the utility poles. Before the conductors are classified, their layout and sag are estimated, converting conductor segmentation into a linear problem; and a 3D to 2D projection is used to improve density and simplify clustering. The work also attempts to reduce the cost of conductor reconstruction by utilising lower-cost vehicle-mounted LiDAR. By avoiding point classification, higher precision can be achieved in scenarios where previous methods have suffered from significantly degraded performance. |
| first_indexed | 2024-03-10T14:54:00Z |
| format | Article |
| id | doaj.art-8a21958907544775baa256b7aa652cab |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T14:54:00Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8a21958907544775baa256b7aa652cab2023-11-20T20:45:39ZengMDPI AGRemote Sensing2072-42922020-11-011222371810.3390/rs12223718Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDARJosh McCulloch0Richard Green1Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New ZealandDepartment of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New ZealandDynamic Line Rating (DLR) is a process which electrical network operators can implement to improve efficiency by dynamically adjusting the load capacity as conditions allow. To implement DLR an accurate model of the conductors and their clearances is needed. Airborne LiDAR, while expensive, is the most common method of collecting line data as it is fast and is of high quality. State of the art methods for automatically reconstructing conductors first classify conductor points before fitting conductor models. This approach works well for high tension lines with significant separation between conductors but tends to perform poorly in urban environments where conductors are packed tightly together and surrounded by clutter. The method presented in this article attempts to overcome these challenges by performing an informed search for the conductors, anchored to the utility poles. Before the conductors are classified, their layout and sag are estimated, converting conductor segmentation into a linear problem; and a 3D to 2D projection is used to improve density and simplify clustering. The work also attempts to reduce the cost of conductor reconstruction by utilising lower-cost vehicle-mounted LiDAR. By avoiding point classification, higher precision can be achieved in scenarios where previous methods have suffered from significantly degraded performance.https://www.mdpi.com/2072-4292/12/22/3718conductor reconstructionurban power linevehicle-mounted LiDAR |
| spellingShingle | Josh McCulloch Richard Green Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR Remote Sensing conductor reconstruction urban power line vehicle-mounted LiDAR |
| title | Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR |
| title_full | Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR |
| title_fullStr | Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR |
| title_full_unstemmed | Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR |
| title_short | Conductor Reconstruction for Dynamic Line Rating Using Vehicle-Mounted LiDAR |
| title_sort | conductor reconstruction for dynamic line rating using vehicle mounted lidar |
| topic | conductor reconstruction urban power line vehicle-mounted LiDAR |
| url | https://www.mdpi.com/2072-4292/12/22/3718 |
| work_keys_str_mv | AT joshmcculloch conductorreconstructionfordynamiclineratingusingvehiclemountedlidar AT richardgreen conductorreconstructionfordynamiclineratingusingvehiclemountedlidar |