Cover
Vol. 14 No. 1 (2018)

Published: June 30, 2018

Pages: 65-79

Original Article

Short Circuit Faults Identification and Localization in IEEE 34 Nodes Distribution Feeder Based on the Theory of Wavelets

Abstract

In this paper a radial distribution feeder protection scheme against short circuit faults is introduced. It is based on utilizing the substation measured current signals in detecting faults and obtaining useful information about their types and locations. In order to facilitate important measurement signals features extraction such that better diagnosis of faults can be achieved, the discrete wavelet transform is exploited. The captured features are then utilized in detecting, identifying the faulted phases (fault type), and fault location. In case of a fault occurrence, the detection scheme will make a decision to trip out a circuit breaker residing at the feeder mains. This decision is made based on a criteria that is set to distinguish between the various system states in a reliable and accurate manner. After that, the fault type and location are predicted making use of the cascade forward neural networks learning and generalization capabilities. Useful information about the fault location can be obtained provided that the fault distance from source, as well as whether it resides on the main feeder or on one of the laterals can be predicted. By testing the functionality of the proposed scheme, it is found that the detection of faults is done fastly and reliably from the view point of power system protection relaying requirements. It also proves to overcome the complexities provided by the feeder structure to the accuracy of the identification process of fault types and locations. All the simulations and analysis are performed utilizing MATLAB R2016b version software package.

References

  1. M. A . Kafiey, “A Wavelet Packet Transform Based on-Line Technique for the Protection of Three-phase Interior Permanent Magnet Motors,” M.S. thesis, Memorial University of Newfoundland, St. John’s, Canada, May 2006.
  2. Sonja Ebron, David L. Lubkeman and Mark White, “ A neural network approach to the detection of incipient faults on power distribution feeders,” IEEE Transactions on Power Delivery , vol. 5, pp. 905-912, April 1990.
  3. Rodrigo Hartstein Salim, Karen Rezende Caino and Arturo Suman Bretas, “ Fault detection in primary distribution systems using wavelets,” in Proc. Power Systems Transients Int. Conf. , France, 2007.
  4. Omar A. S. Youssef, “Combined fuzzy-logic wavelet-based fault classification technique for power system relaying,” 589, April 2004.
  5. Mamta Patel and R. N. Patel, “ Fault analysis in transmission lines using neural network and wavelets,” in Proc. 2015 Signal Processing and Integrated Networks (SPIN) Int. Conf ., India, 2015, pp. 719-724.
  6. Majid Jamil, Rajveer Singh, Sanjeev Kumar Sharma, “Fault combined discrete wavelet transform and fuzzy logic,” Journal of Electrical Systems and Information Technology , Vol. 2, pp. 257-267, September 2015.
  7. Karen L. Butler and Dr. James A. Momoh, “Detection and classification of line faults on power distribution systems using neural networks,” in Proc. 36 th Midwest Symposium on Circuits and Systems , USA, 1993, pp. 368-371.
  8. Rameshkumar C. Mishra and P.M. Deoghare, “Analysis of transmission line fault by using wavelet,” International journal of engineering research & technology , vol. 3, pp. 36-40, May 2014.
  9. Suman Devi, Nagendra K. Swarnkar, Sheesh Ram Ola and Om Prakash Mahela, “Detection of Transmission Line Faults Using Discrete Wavelet Transform,” in Proc. 2016 Advances in signal processing Conf. (CASP) , India, 2016, pp. 133-138.
  10. A. Ngaopitakkul, C. Apisit, C. Pothisarn, C. Jettanasen and S. Jaikhan, “Identification of fault locations in underground distribution system using discrete wavelet transform,” in Proc. 2010 Engineers and Computer Scientists Int. Multi Conf. , Hong Kong, 2010.
  11. Liqun Shang, Wensong Zhai and Pei Liu, “ Study of Fault Location in Transmission Line Using S Transform,” in Proc. 2016 Computer, Consumer and Control Conf. (IS3C) , China 2016, pp. 85-88.
  12. R. Das, M. S. Sachdev and T. S. Sidue, “ A Fault Locator for Radial Subtransmission and Distribution Lines,” IEEE Power Engineering Society Summer Meeting , USA, July 2000, pp. 443-448.
  13. Rahman Dashti, Mohammad Daisy and Hamid Reza Shaker, “A new Method presentation for locating Fault in Power Distribution Networks,” in Proc. 2016 Electrical Apparatus and Technologies International Symposium (SIELA) , Bulgaria, June 2016.
  14. H. Mokhlis and H. Y. Li, “Fault location estimation for distribution system using simulated voltage sags data,” in Proc. 2007 Universities Power Engineering Conf. (UPEC) , UK, Sept. 2007, pp. 242-247.
  15. A. H. A. Bakar, M. S. Ali, ChiaKwang Tan, H. Mokhlis and H. Arof, H. A. Illias, “ High impedance fault location transforms,” International Journal of Electrical Power & Energy Systems , vol. 55, pp. 723-730, 2014.
  16. J. J. Mora, G. Garrillo and L. Pérez, “Fault Location in Power Distribution System using ANFIS Nets and Current Patterns,” in Proc. 2006 Transmission & Distribution Conference and Exposition , Venezuela, Aug. 2006, pp. 1-6.
  17. A. Abdollahi and S. Seyedtabaii, “Transmission Line Fault Location Estimation by Fourier & Wavelet Transforms Using ANN,” in Proc. 2010 4 th International Power Engineering and Optimization Conference (PEOCO) , Malaysia, June 2010, pp. 573-578
  18. Md. Abdul Kalam, Majid Jamil and A. Q. Ansari, “Wavelet based ANN Approach for Fault Location on a Transmission Line,” in Proc. 2010 Power Electronics, Drives and Energy Systems (PEDES) & Power India Joint
  19. S. Bunjongjit, A. Ngaopitakkul and C. Pothisarn, “A Discrete Wavelet Transform and Fuzzy Logic Algorithm for Distribution System,” in Proc. 2013 Fuzzy Theory and Its Application (iFUZZY) Int. Conf. , Taiwan, Dec. 2013, pp. 415-419.
  20. Kapildev Lout and Raj K. Aggarwal, “A feedforward artificial neural network approach to fault classification and location on a 132kv transmission line using current signals only,” in Proc. 2012 47 th Universities Power Engineering Conference (UPEC) Int. Conf ., UK, Sept. 2012.
  21. Binoy Saha, Bikash Patel, Parthasarathi Bera, “ DWT and BPNN Based Fault Detection, Classification and Estimation of Location of HVAC Transmission Line,” in Proc. 2016
  22. Zhengyou He, Xiaopeng Li, and Shuang Chen, “ A travelling wave natural frequency based single ended fault location method with unknown equivalent system Energy Systems , vol. 26, pp. 509-524, 2016.
  23. Sunil Singh and D.N. Vishwakarma, “ Application of DWT and ANN for fault classification and location in a series compensated transmission line,” in Proc. 2016 IEEE 6 th International Conference on Power Systems(ICPS), India, March 2016.
  24. Sadiq I. Hassan, Adil A. Obed, and Khalid M. AbdulHassan, “Practical for stator faults Protection and diagnosis in 3-ph IM based on WPT and neural network,” The International Journal of Engineering and Science (IJES) , vol. 5, pp. 52-67, 2016.
  25. Martin L. Baughman, Chen-Ching Liu and Roger C. Dugan. (August 2013). IEEE 34 node test feeder. IEEE PES Power & Energy Society . [Online]. Available: https://www.ewh.ieee.org/soc/pes/dsacom/testfeeders/feeder 34.zip.
  26. Q. Fu, A. Solanki, L. F. Montoya, A. Nasiri, V. Bhavaraju, T. Abdallah, D. Yu, “ Generation Capacity Design for a Microgrid for Measurable Power Quality Indexes, ” in Proc. 2012 IEEE PES Inovative Smart Grid Technologies (ISGT) , USA, April 2012, pp. 1-6.
  27. Ndaga Mwakabuta and Arun Sekar, “Comparative Study of the Node Test Feeder under Practical Simplifications, ” in Proc. 2007 39 th North American Power Symposium , USA, Oct. 2007, pp. 484-491.
  28. Rodrigo Hartstein Salim, Karen Rezende Caino De Oliveira and Arturo Suman Bretas, “Fault detection in primary distribution systems using wavelets,” in Proc. Power Systems Transients Int. Conf. , France, June 2007.
  29. F. Martin, J. A. Aguado, M. Medina, and M. Munoz, “Classification of faults in double circuit lines using wavelet transforms,” in proc. 2008 IEEE Int. Conf. on
  30. P. K. Kankar, Satish C. Sharma, and S. P. Harsha, “ Fault diagnosis of ball bearings using continuous wavelet transform,” Applied Soft Computing, vol. 11, pp. 23002312, 2011.
  31. Kumar H S, P. Srinivasa Pai, N. S. Sriram, and Vijay Gs, “ Selection of mother wavelet for effective wavelet transform of bearing vibration signals,” Advanced Materials Research, vol. 1039, pp. 169-176, 2014. [32 ] Mark Hudson Beale, Martin T. Hagan and Howard B. Demuth, Neural Network Toolbox TM User’s Guide , The Math Works Inc., 2016, ch. 3, pp. 1-30.
  32. S. H. Horowitz, and A. G. Phadke, Power system relaying , 3 rd Ed., Baldoack, Hertfordshire, England: Wiley, 2008. ch. 1, pp.7-11.