Development of Transmission Line Failure Rate Model using Polynomial Regression

  • Authors

    • M K.N Arshad
    • N Aminudin
    • M Marsadek
    • S Z.M Noor
    • R H Salimin
    • D Johari
    2018-08-13
    https://doi.org/10.14419/ijet.v7i3.15.17508
  • OLS, Polynomial model, probability estimation, transmission line outage, transmission line failure rate model.
  • Drastic climate change and more frequent occurrences of natural disaster which destruct power system infrastructure results in power delivery congestion at the transmission network. Heavily loaded transmission network that operates during adverse weather is very prone to outage, hence may trigger more critical problem such as voltage collapse. Research on risk of voltage collapse due to transmission line outage has been carried out by numerous researcher. Generally, this risk study involves two major parts; one is the assessment of voltage collapse impact due to the line outage and the other is the assessment of probability of line outage to occur. According to many literatures, precise probability estimation is very difficult to be evaluated since it is very unpredictable. Therefore, serious attention and studies have been focused in estimating the probability of transmission line outage prudently. The accuracy of probability assessed using Poisson distribution is very much dependent on its failure rate value. In this research, a weather-based transmission line failure rate model is developed using Ordinary Least Square (OLS) polynomial regression technique. To evaluate the effectiveness of the proposed method, comparative study with previous research which utilized robust MM-estimator technique is conducted. The results revealed that the proposed technique is more precise and the weather considered in the study has more significant impact compared to the preceding work. Thus, this finding contributes to more accurate probability estimation in risk of voltage collapse assessment.

     


  • References

    1. [1] R. Billinton and L. Wenyuan, “Hybrid approach for reliability evaluation of composite generation and transmission systems using Monte-Carlo simulation and enumeration technique,†IEE Proc. C Gener. Transm. Distrib., vol. 138, no. 3, p. 233, 1991.

      [2] W. Li, Risk assessment of power systems models, methods and applications. Canada: John Wiley & Sons Inc., 2005.

      [3] J. McCalley et al., “Probabilistic security assessment for power system operations,†in IEEE Power Engineering Society General Meeting, 2004., 2004, p. 212–220 Vol.1.

      [4] N. Ming, J. D. McCalley, V. Vittal, and T. Tayyib, “Online risk-based security assessment,†IEEE Trans. Power Syst., vol. 18, no. 1, pp. 258–265, 2003.

      [5] W. Hua, J. D. McCalley, and V. Vittal, “Risk based voltage security assessment,†IEEE Trans. Power Syst., vol. 15, no. 4, pp. 1247–1254, 2000.

      [6] J. D. McCalley, V. Vittal, H. Wan, Y. Dai, and N. Abi-Samra, “Voltage risk assessment,†in IEEE Power Engineering Society Summer Meeting, 1999. , 1999, vol. 1, pp. 179–184 vol.1.

      [7] C. Ma, X. Y. Xiao, C. S. Li, Y. Zhang, and H. Q. Li, “Uncertain risk assessment model for catastrophic accidents in power system,†Int. J. Electr. Power Energy Syst., vol. 62, pp. 374–382, 2014.

      [8] X. Fei and J. D. McCalley, “Power System Risk Assessment and Control in a Multiobjective Framework,†IEEE Trans. Power Syst., vol. 24, no. 1, pp. 78–85, 2009.

      [9] F. Yongqing, W. Wenchuan, Z. Boming, and L. Wenyuan, “Power System Operation Risk Assessment Using Credibility Theory,†IEEE Trans. Power Syst., vol. 23, no. 3, pp. 1309–1318, 2008.

      [10] F. Weihui, Z. Sanyi, J. D. McCalley, V. Vittal, and N. Abi-Samra, “Risk assessment for special protection systems,†IEEE Trans. Power Syst., vol. 17, no. 1, pp. 63–72, 2002.

      [11] F. Weihui, J. D. McCalley, and V. Vittal, “Risk assessment for transformer loading,†IEEE Trans. Power Syst., vol. 16, no. 3, pp. 346–353, 2001.

      [12] L. Ning, W. Wu, B. Zhang, and P. Zhang, “A time-varying transformer outage model for on-line operational risk assessment,†Int. J. Electr. Power Energy Syst., vol. 33, no. 3, pp. 600–607, 2011.

      [13] P. Henneaux, P. E. Labeau, and J. C. Maun, “Blackout probabilistic risk assessment and thermal effects: Impacts of changes in generation,†IEEE Trans. Power Syst., vol. 28, no. 4, pp. 4722–4731, 2013.

      [14] X. Fei, J. D. McCalley, Y. Ou, J. Adams, and S. Myers, “Contingency Probability Estimation Using Weather and Geographical Data for On-Line Security Assessment,†in International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 2006., 2006, pp. 1–7.

      [15] A. M. Leite da Silva, I. P. Coutinho, A. C. Zambroni de Souza, R. B. Prada, and A. M. Rei, “Voltage collapse risk assessment,†Electr. Power Syst. Res., vol. 54, no. 3, pp. 221–227, 2000.

      [16] V. Krishnan and J. D. McCalley, “Contingency assessment under uncertainty for voltage collapse and its application in risk based contingency ranking,†Int. J. Electr. Power Energy Syst., vol. 43, no. 1, pp. 1025–1033, 2012.

      [17] P. Kankanala, A. Pahwa, and S. Das, “Regression models for outages due to wind and lightning on overhead distribution feeders,†in Power and Energy Society General Meeting, 2011 IEEE, 2011, pp. 1–4.

      [18] Z. Yujia, A. Pahwa, and Y. Shie-Shien, “Modeling Weather-Related Failures of Overhead Distribution Lines,†Power Syst. IEEE Trans., vol. 21, no. 4, pp. 1683–1690, 2006.

      [19] X. Chen, Y. Wu, and S. Lou, “Risk assessment for power system static security based on fuzzy modeling of weather conditions,†in 2011 IEEE Power Engineering and Automation Conference, 2011, vol. 1, no. 1, pp. 367–371.

      [20] X. Song, Z. Wang, H. Xin, and D. Gan, “Risk-based dynamic security assessment under typhoon weather for power transmission system,†Asia-Pacific Power Energy Eng. Conf. APPEEC, no. 1, pp. 1–6, 2013.

      [21] N. Aminudin, M. Marsadek, N. M. Ramli, T. K. A. Rahman, and N. Razali, “Robust model for weather-related contingency probability estimation used for risk based security assessment,†Int. Rev. Model. Simulations, vol. 7, no. 5, 2014.

      [22] R. E. Schumacker, M. P. Monahan, and R. E. Mount, “A Comparison of OLS and Robust Regression using S-Plus,†in Multiple Linear Regression Viewpoints, 2002, vol. 28(2).

  • Downloads

  • How to Cite

    K.N Arshad, M., Aminudin, N., Marsadek, M., Z.M Noor, S., H Salimin, R., & Johari, D. (2018). Development of Transmission Line Failure Rate Model using Polynomial Regression. International Journal of Engineering & Technology, 7(3.15), 91-94. https://doi.org/10.14419/ijet.v7i3.15.17508