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Autonomous Drone-Based Powerline Insulator Inspection via Deep Learning

  • Anas Muhammad
  • Adnan Shahpurwala
  • Shayok MukhopadhyayEmail author
  • Ayman H. El-Hag
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1092)

Abstract

Accumulation of pollutants on ceramic insulators is one of the major causes of dry band arcing, a predecessor to flashovers, which may further cause major outages of electricity. It is critical to know locations of polluted insulators to prevent flashovers to make the power-grid reliable. This paper proposes a solution to detect the location of polluted insulators along an overhead transmission line using a quadcopter. Once provided with the GPS locations of the electrical powerline transmission towers, the quadcopter autonomously hovers along the line. And while doing so, it sends a live video feed of the transmission line to the ground station. A pre-trained neural network on the ground station then detects insulators in the video and classifies the detected insulators as polluted or clean. Only if the insulator detected is polluted, its location is recorded and reported back to the ground station. The novelty of this work is the use of a drone to automate the process of insulator inspection via a deep learning based neural network approach. Experiments show that accurate inspection results are obtained. This work is an initial step in the direction of achieving completely autonomous drone-based powerline insulator inspection.

Keywords

Drone Quadcopter Overhead transmission line (OHTL) Insulator Pollution Inspection Autonomous Deep learning Neural network 

References

  1. 1.
    Cavallini, A., Chandrasekar, S., Montanari, G.C., Puletti, F.: Inferring ceramic insulator pollution by an innovative approach resorting to PD detection. IEEE Trans. Dielectr. Electr. Insul. 14(1), 23–29 (2007)CrossRefGoogle Scholar
  2. 2.
    Charniak, E.: Introduction to Deep Learning. The MIT Press, Cambridge (2019)Google Scholar
  3. 3.
    Fangzheng, Z., Wanguo, W., Yabo, Z., Peng, L., Qiaoyun, L., Lingao, J.: Automatic diagnosis system of transmission line abnormalities and defects based on UAV. In: 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI), pp. 1–5, October 2016Google Scholar
  4. 4.
    Fontana, E., Martins-Filho, J.F., Oliveira, S.C., Cavalcanti, F.J.M.M., Lima, R.A., Cavalcanti, G.O., Prata, T.L., Lima, R.B.: Sensor network for monitoring the state of pollution of high-voltage insulators via satellite. IEEE Trans. Power Delivery 27(2), 953–962 (2012)CrossRefGoogle Scholar
  5. 5.
    Karakose, E.: Performance evaluation of electrical transmission line detection and tracking algorithms based on image processing using UAV. In: 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1–5, September 2017Google Scholar
  6. 6.
    Khalyasmaa, A.I., Dmitriev, S.A., Romanov, A.M.: Robotic intelligence laboratory for overhead transmission lines assessment. In: 2016 57th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), pp. 1–6 (2016)Google Scholar
  7. 7.
    Li, H., Wang, B., Liu, L., Tian, G., Zheng, T., Zhang, J.: The design and application of SmartCopter: an unmanned helicopter based robot for transmission line inspection. In: 2013 Chinese Automation Congress, pp. 697–702 (2013)Google Scholar
  8. 8.
    Lijun, J., Jianyong, A., Tian, Z., Kai, G., Hua, H.: Pollution state detection of insulators based on multisource imaging and information fusion. In: 2016 IEEE International Conference on Dielectrics (ICD), pp. 544–547, July 2016Google Scholar
  9. 9.
    Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: SSD: single shot MultiBox detector. In: Computer Vision – ECCV 2016, pp. 21–37. Springer International Publishing (2016)Google Scholar
  10. 10.
    Lv, L., Li, S., Wang, H., Jin, L.: An approach for fault monitoring of insulators based on image tracking. In: 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), pp. 1–6, November 2017Google Scholar
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    Tiantian, Y., Yang, G., Yu, J.: Feature fusion based insulator detection for aerial inspection. In: 36th Chinese Control Conference (CCC), pp. 10972–10977 (2017)Google Scholar
  15. 15.
    Wang, L., Wang, H.: A survey on insulator inspection robots for power transmission lines. In: 2016 4th International Conference on Applied Robotics for the Power Industry (CARPI), pp. 1–6, October 2016Google Scholar
  16. 16.
    Werneck, M.M., dos Santos, D.M., de Carvalho, C.C., de Nazaré, F.V.B., da Silva Barros Allil, R.C.: Detection and monitoring of leakage currents in power transmission insulators. IEEE Sens. J. 15(3), 1338–1346 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Anas Muhammad
    • 1
  • Adnan Shahpurwala
    • 1
  • Shayok Mukhopadhyay
    • 1
    Email author
  • Ayman H. El-Hag
    • 2
  1. 1.Department of Electrical EngineeringAmerican University of SharjahSharjahUAE
  2. 2.Department of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada

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