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Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

  • Lee, Junghoon (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Jung, Ho-Yeon (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Koo, J.R. (KEPCO Research Institute) ;
  • Yoon, Yoonjin (Dept. of Civil and Environmental Engineering, KAIST) ;
  • Jung, Hyung-Jo (Dept. of Civil and Environmental Engineering, KAIST)
  • Received : 2016.09.08
  • Accepted : 2017.01.03
  • Published : 2017.03.01

Abstract

Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.

Keywords

Galloping;Prediction;Interpolation;Logistic regression

Acknowledgement

Supported by : Korea Electric Power Corporation (KEPCO)

References

  1. Farzaneh, M., Atmospheric icing of power networks, Springer Science & Business Media, 2008.
  2. Lilien, J.-L., State of the art of conductor galloping, CIGRE, 2007.
  3. Z. K.-j. F. Dong-jie and W. J.-c. S. Na, "The Galloping and Its Preventing Techniques on Overhead Transmission Line," Electrical Equipment, vol. 6, pp. 6, 2008.
  4. C. S. Yoon, J. R. Koo, and H. H. Sung, Prevention of Galloping Accident through Install Standard for Spacer on Transmission Power Line, Korea Electric Power Corporation, Republics of Korea, 2010.
  5. J. Den Hartog, "Transmission line vibration due to sleet," Transactions of the American Institute of Electrical Engineers, vol. 4, pp. 1074-1076, 1932.
  6. K. E. Gawronski and R. J. Hawks, "Computer simulation of galloping catenaries," Electric Power Systems Research, vol. 1, pp. 283-289, 1978. https://doi.org/10.1016/0378-7796(78)90014-7
  7. R. Keutgen and J.-L. Lilien, "Benchmark cases for galloping with results obtained from wind tunnel facilities validation of a finite element model," Power Delivery, IEEE Transactions on, vol. 15, pp. 367-374, 2000. https://doi.org/10.1109/61.847275
  8. M. Kermani, M. Farzaneh, and L. E. Kollar, "The Effects of Wind Induced Conductor Motion on Accreted Atmospheric Ice," Power Delivery, IEEE Transactions on, vol. 28, pp. 540-548, 2013. https://doi.org/10.1109/TPWRD.2013.2244922
  9. J. Hu, B. Yan, S. Zhou, and H. Zhang, "Numerical investigation on galloping of iced quad bundle conductors," Power Delivery, IEEE Transactions on, vol. 27, pp. 784-792, 2012. https://doi.org/10.1109/TPWRD.2012.2185252
  10. M. S. Tehrany, B. Pradhan, and M. N. Jebur, "Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS," Journal of Hydrology, vol. 504, pp. 69-79, 2013 https://doi.org/10.1016/j.jhydrol.2013.09.034
  11. M.-L. Guillerminet and R. S. Tol, "Decision making under catastrophic risk and learning: the case of the possible collapse of the West Antarctic Ice Sheet," Climatic Change, vol. 91, pp. 193-209, 2008. https://doi.org/10.1007/s10584-008-9447-4
  12. D. T. Bui, B. Pradhan, O. Lofman, I. Revhaug, and O. B. Dick, "Regional prediction of landslide hazard using probability analysis of intense rainfall in the Hoa Binh province, Vietnam," Natural hazards, vol. 66, pp. 707-730, 2013. https://doi.org/10.1007/s11069-012-0510-0
  13. R. Diao, K. Sun, V. Vittal, R. J. O'Keefe, M. R. Richardson, N. Bhatt, et al., "Decision tree-based online voltage security assessment using PMU measurements," Power Systems, IEEE Transactions on, vol. 24, pp. 832-839, 2009. https://doi.org/10.1109/TPWRS.2009.2016528
  14. P. Kankar, S. C. Sharma, and S. Harsha, "Fault diagnosis of ball bearings using machine learning methods," Expert Systems with Applications, vol. 38, pp. 1876-1886, 2011. https://doi.org/10.1016/j.eswa.2010.07.119
  15. Menard, S., Applied logistic regression analysis, Sage, 2002.