DOI QR코드

DOI QR Code

A Novel Algorithm for Fault Type Fast Diagnosis in Overhead Transmission Lines Using Hidden Markov Models

  • Jannati, M. (Department of Electrical Engineering, Amirkabir University of Technology) ;
  • Jazebi, S. (Department of Electrical Engineering, Amirkabir University of Technology) ;
  • Vahidi, B. (Department of Electrical Engineering, Amirkabir University of Technology) ;
  • Hosseinian, S.H. (Department of Electrical Engineering, Amirkabir University of Technology)
  • Received : 2010.09.18
  • Accepted : 2011.09.19
  • Published : 2011.11.01

Abstract

Power transmission lines are one of the most important components of electric power system. Failures in the operation of power transmission lines can result in serious power system problems. Hence, fault diagnosis (transient or permanent) in power transmission lines is very important to ensure the reliable operation of the power system. A hidden Markov model (HMM), a powerful pattern recognizer, classifies events in a probabilistic manner based on fault signal waveform and characteristics. This paper presents application of HMM to classify faults in overhead power transmission lines. The algorithm uses voltage samples of one-fourth cycle from the inception of the fault. The simulation performed in EMTPWorks and MATLAB environments validates the fast response of the classifier, which provides fast and accurate protection scheme for power transmission lines.

References

  1. C. E. J. Bowler, P. G. Brown, and D.N. walker, "Evaluation of The Effect of Power Circuit Break Reclosing Practices on Turbine-Generator Shaft," IEEE Trans. Power App. Syst., vol. PAS-99, pp.1764-1779, Sept. 1980. https://doi.org/10.1109/TPAS.1980.319766
  2. Mohsen Jannati, Behroz Vahidi, Seyed Hossein Hosseinian and Hamid Reza Baghaee, "A Novel Approach for Optimizing Dead Time of Extra High Voltage Transmission Lines" Optimization of Electrical and Electronic Equipment, OPTIM. 11th International Conference on (IEEE), pp.215 - 220, May. 2008.
  3. Z. M. Rajovedic and J. R. Shin, "New One Terminal Digital Algorithm for Adaptive Reclosing and Fault Distance Calculation on Transmission Line," IEEE Trans. Power Del., vol.21, no.3, pp.37-41, July. 2006.
  4. Mohsen Jannati, Behroz Vahidi, Seyed Hossein Hosseinian and Hamid Reza Baghaee, "A New Adaptive Single Phase Auto-Reclosure Scheme for EHV Transmission Lines" Power System Conference, MEPCON. 12th International Middle-East (IEEE), pp.203 - 207, March. 2008.
  5. Sang-Pil Ahn, Chul-Hwan Kim, R.K. Aggarwal and A.T. Johns, "An Alternative Approach to Adaptive Single Pole Auto-Reclosing in High Voltage Transmission Systems Based on Variable Dead Time Control," IEEE Trans. Power Del., vol.16, pp.676-686, Oct. 2001.
  6. Z.Q. Bo, R. K. Aggarwal and A. T. Johns, "A Novel Technique to Distinguish Between Transient and Permanent Fault Based on Detection of Current Transients," Proceeding of 4th International Conference on Advances in Power System Control and Management, pp.217-220, APSCOM-97, Hong Kong, Nov. 1997.
  7. Ge Yaozhang, Sui Fanghai and Xiao Yuan, "Prediction Method for Preventing Single-Phase Reclosing on Permanent Faults," IEEE Trans. Power Del., vol.4, pp.114-121, Jan. 1989. https://doi.org/10.1109/61.19197
  8. N. I. Elkalashy, H. A. Darwish, A. M. I. Taalab, M.A. Izzularab, "An Adaptive Single Pole Autoreclosure Based on Zero Sequence Power," Electric Power System Research., vol.77, pp.438-446, 2007. https://doi.org/10.1016/j.epsr.2006.04.006
  9. R. K. Aggarwal, A. T. Johns, Dunn. R.W, Fitton. D.S, "Neural Network based Adaptive Sigle-Pole Autoreclosure Technique for EHV extinguishing time of secondary arc and fault location in PTL Transmission System," IEE Proc- Gener. Transm. Distrib., vol.141, pp.155-160, March. 1994. https://doi.org/10.1049/ip-gtd:19949864
  10. Majid Sanaye-Pasand and Ali kadivar, "Design of An Online Adaptive Auto-Reclose Algorithm for HV Power transmission lines," IEEE Power India Conference, Apr. 2006.
  11. I. K. Yu, Y. H. Song, "Wavelet Transform and Neural Network Approach to Developing Adaptive Single-Pole Auto-Reclosing Schemes for EHV Transmission Systems," IEEE Power Engineering Review, pp.62-64, Nov. 1998.
  12. Xiangning Lin, Pei Liu, "Method of Distinguishing Between Instant and Permanent Fault of Transmission Lines Based on Fuzzy Decision," IEEE Catalog No. 98EX137, vol.2, pp.455-460, 1998.
  13. Zoran M. Radojevic and Joong-Rin Shin, "New Digital Algorithm for Adaptive Reclosing Based on The Calculation of The Faulted Phase Voltage Total Harmonic Distortion Factor," IEEE Trans. Power Del., vol.22, pp.37-41, Jan. 2007. https://doi.org/10.1109/TPWRD.2006.886781
  14. B. Vahidi, M. Jannati and S. H. Hosseinian, "A Novel Approach to Adaptive Single Phase Autoreclosure Scheme for EHV Power Transmission Lines Based on Learning Error Function of ADALINE" Simulation, vol. 84, no.12, pp 601-610, 2008. https://doi.org/10.1177/0037549708097293
  15. R. Lawrance and A. Rabiner "A Tutorial on hidden Markov models and selected application in speech recognition", Proc. Of IEEE, vol.77, pp.257-285, 1989. https://doi.org/10.1109/5.18626
  16. Jaehak Chung, E. J. Powers, W. M. Grady and S. C. Bhatt "Power disturbance classifier using a rulebased method and wavelet packet-based hidden Markov model", IEEE Trans. Power Del., vol.17, pp.233 - 241, 2002. https://doi.org/10.1109/61.974212
  17. T. K. Abdel-Galil, A. M. Youssef and M. M. A. Salama "Disturbance Classification Using Hidden Markov Models and Vector Quantization", IEEE Trans. Power Del. vol.20, pp.2129- 2135, 2005. https://doi.org/10.1109/TPWRD.2004.843399
  18. Abdel-Galil, Y. G. Hegazy, M. M. A Salama and R. Bartnikas "Partial discharge pulse pattern recognition using Hidden Markov Models", IEEE Trans. Dielectrics and Electrical Insulation, vol.11, pp.715 - 723, 2004. https://doi.org/10.1109/TDEI.2004.1324361
  19. K. C. Kwon and J. H. Kim "Accident identification in nuclear power plants using hidden Markov models", EAAI, vol.12, pp.491-501, 1999.
  20. A. M. Gonzalez, A. M. S. Roque and J.Garcia-Gonzalez "Modeling and forecasting electricity prices with input/output hidden Markov models", IEEE Trans Power Syst., vol.20, pp.13-24, Jan. 2005. https://doi.org/10.1109/TPWRS.2004.840412
  21. W. Yu and G. B. Sheble "Modeling electricity markets with hidden Markov model", EPSR, vol.76, pp.445-451, 2006.
  22. M. R. Hassan, B. Nath and M. Kireley "A fusion model of HMM, ANN and GA for stock market forecasting", ESA, vol.33, pp.171-180, 2007.
  23. Qian Suxiang, Jiao Weidong, Hu Hongsheng and Yan Gongbiao, "Transformer Power Fault Diagnosis System Design Based on the HMM Method", IEEE International Conference on Automation and Logistics, pp.1077-1082, 2007.
  24. X. Ma and J. Shi "A new method for discrimination between fault and magnetizing inrush current using HMM", EPSR, vol.56, pp.43-49, 2000.
  25. S. Jazebi, B. Vahidi and S. H. Hosseinian, "A Novel Discriminative Approach Based on Hidden Markov Models and Wavelet Transform to Transformer Protection", Simulation, vol.86, no.2, pp.93-107, 2010. https://doi.org/10.1177/0037549709342729

Cited by

  1. An enhanced ACO and PSO based fault identification and rectification approaches for FACTS devices vol.27, pp.8, 2017, https://doi.org/10.1002/etep.2344