The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 73–77, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-73-2016
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 73–77, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-73-2016

  22 Jun 2016

22 Jun 2016

ESTIMATION OF INSULATOR CONTAMINATIONS BY MEANS OF REMOTE SENSING TECHNIQUE

Ge Han1, Wei Gong2,3, Xiaohui Cui1, Miao Zhang4, and Jun Chen5 Ge Han et al.
  • 1Wuhan University, International School of Software, 430079 Luoyu Road 129, Wuhan, China
  • 2Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430079 Luoyu Road 129, Wuhan, China
  • 3Collaborative Innovation Center of Geospatial Technology, 430079 Luoyu Road 129, Wuhan, China
  • 4School of Environmental Science and Tourism, Nanyang Normal University, Wolong Road No.1638, Nan Yang 473061,China
  • 5Wuhan University, School of Computer, 430079 Luoyu Road 129, Wuhan, China

Keywords: Insulator Contaminations, Power Facilities, ESDD, Remote Sensing, SVM

Abstract. The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.