Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
권호 표지
DOI QR코드

DOI QR Code

Automatic Detection of Malfunctioning Photovoltaic Modules Using Unmanned Aerial Vehicle Thermal Infrared Images

  • Kim, Dusik (Korea Institute of Civil Engineering and Building Technology, ICT Convergence and Integration Research Institute) ;
  • Youn, Junhee (Korea Institute of Civil Engineering and Building Technology, ICT Convergence and Integration Research Institute) ;
  • Kim, Changyoon (Korea Institute of Civil Engineering and Building Technology, ICT Convergence and Integration Research Institute)
  • Received : 2016.11.28
  • Accepted : 2016.12.26
  • Published : 2016.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Cells of a PV (photovoltaic) module can suffer defects due to various causes resulting in a loss of power output. As a malfunctioning cell has a higher temperature than adjacent normal cells, it can be easily detected with a thermal infrared sensor. A conventional method of PV cell inspection is to use a hand-held infrared sensor for visual inspection. The main disadvantages of this method, when applied to a large-scale PV power plant, are that it is time-consuming and costly. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule was applied to automatically detect defective panels using the mean intensity and standard deviation range of each panel by array. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97% or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule. In this study, we used a panel area extraction method that we previously developed; fault detection accuracy would be improved if panel area extraction from images was more precise. Furthermore, the proposed algorithm contributes to the development of a maintenance and repair system for large-scale PV power plants, in combination with a geo-referencing algorithm for accurate determination of panel locations using sensor-based orientation parameters and photogrammetry from ground control points.

Keywords

References

  1. Bazilian, M.D., Kamalanathan, H., and Prasad, D.K. (2002), Thermographic analysis of a building integrated photovoltaic system, Renewable Energy, Vol. 26, No. 3, pp. 449-461. https://doi.org/10.1016/S0960-1481(01)00142-2
  2. Buerhop, C. and Scheuerpflug, H. (2014), Field inspection of PV-modules using aerial, drone-mounted thermography, Proceedings of the 29th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC), 22-26 September, Amsterdam, Netherlands, pp. 2975-2979.
  3. Canny, J. (1986), A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 8, No. 6, pp. 679-698.
  4. Gonzalez, R.C., Woods, R.E., and Eddins, S.L. (2004), Digital Image Processing using Matlab, Pearson Prentice Hall, N.J.
  5. Grimaccia, F., Aghaei, M., Mussetta, M., Leva, S., and Quater, P.B. (2015), Planning for PV plant performance monitoring by means of unmanned aerial systems (UAS), International Journal of Energy and Environmental Engineering, Vol. 6, No. 1, pp. 47-54. https://doi.org/10.1007/s40095-014-0149-6
  6. Kim, D., Youn, J., and Kim, C. (2016a), Determination of defective photovoltaic module by using thermography, Proceedings of the 37th Asian Conference on Remote Sensing (ACRS), 17-21 October, Colombo, Sri Lanka.
  7. Kim, D., Youn, J., and Kim, C. (2016b), Automatic photovoltaic panel area extraction from uav thermal infrared images, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 34, No. 6, pp. 559-568. https://doi.org/10.7848/ksgpc.2016.34.6.559
  8. Kontges, M., Kurtz, S., Packard, C., Jahn, U., Berger, K.A., Kato, K., Friesen, T., Liu, H., Iseghem, M.V., Wohlgemuth, J., Miller, D., Kempe, M., Hacke, P., Reil, F., Bogdanski, N., Herrmann, W., Buerhop-Lutz, C., Razongles, G., and Friesen, G. (2014), Review of Failures of Photovoltaic Modules, Report IEA-PVPS T13-01:2014, International Energy Agency Photovoltaic Power Systems Programme (IEA-PVPS), Switzerland, pp. 33-35.
  9. McGlone, J.C., Mikhail, E.M., and Bethel, J.S. (2004), Manual of Photogrammetry, American Society of Photogrammetry and Remote Sensing, pp. 969-970.
  10. Quater, P.B., Grimaccia, F., Leva, S., Mussetta, M., and Aghaei, M. (2014), Light Unmanned Aerial Vehicles (UAVs) for Cooperative Inspection of PV Plants, IEEE Journal of Photovoltaics, Vol. 4, No. 4, pp. 1107-1113. https://doi.org/10.1109/JPHOTOV.2014.2323714
  11. Rogotis, S., Ioannidis, D., Tsolakis, A., Tzovaras, D., and Likothanassis, S. (2014), Early defect diagnosis in installed PV modules exploiting spatio-temporal information from thermal images, Proceedings of the 12th Quantitative InfraRed Thermography Conference (QIRT), 7-11 July, Bordeaux, France.
  12. Shin, W.-G., Jung, T.-H., Go, S.-H., Ju, Y.-C., Chang, H.-S., and Kang, G.-H. (2015), Analysis of thermal & electrical characteristics variation of PV module with damaged bypass diodes, Journal of the Korean Solar Energy Society, Vol. 35, No. 4, pp. 67-75. https://doi.org/10.7836/kses.2015.35.4.067
  13. Tsanakas, J.A., Chrysostomou, D., Botsaris, P.N., and Gasteratos, A. (2015), Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements, International Journal of Sustainable Energy, Vol. 34, No. 6, pp. 351-372. https://doi.org/10.1080/14786451.2013.826223