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Title: Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

Abstract

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). In conclusion, wemore » applied our approach to a study area in Minnesota.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3];  [3]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Univ. of Tennessee, Knoxville, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1471940
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
GIScience & Remote Sensing
Additional Journal Information:
Journal Volume: 55; Journal Issue: 6; Journal ID: ISSN 1548-1603
Publisher:
Taylor & Francis
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; electricity; transmission network; machine learning; GIS; open data; voltage ratings

Citation Formats

Schmidt, Erik H., Bhaduri, Budhendra L., Nagle, Nicholas N., and Ralston, Bruce A. Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery. United States: N. p., 2018. Web. doi:10.1080/15481603.2018.1460933.
Schmidt, Erik H., Bhaduri, Budhendra L., Nagle, Nicholas N., & Ralston, Bruce A. Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery. United States. doi:10.1080/15481603.2018.1460933.
Schmidt, Erik H., Bhaduri, Budhendra L., Nagle, Nicholas N., and Ralston, Bruce A. Thu . "Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery". United States. doi:10.1080/15481603.2018.1460933. https://www.osti.gov/servlets/purl/1471940.
@article{osti_1471940,
title = {Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery},
author = {Schmidt, Erik H. and Bhaduri, Budhendra L. and Nagle, Nicholas N. and Ralston, Bruce A.},
abstractNote = {For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). In conclusion, we applied our approach to a study area in Minnesota.},
doi = {10.1080/15481603.2018.1460933},
journal = {GIScience & Remote Sensing},
issn = {1548-1603},
number = 6,
volume = 55,
place = {United States},
year = {2018},
month = {4}
}

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Works referenced in this record:

Induction of decision trees
journal, March 1986