New fault zone identification scheme for busbar using support vector machine

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New fault zone identification scheme for busbar using support vector machine

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This study presents a new support vector machine (SVM)-based fault zone identification scheme for busbar which correctly identifies faults occurring inside and outside the protection zone of busbar. The proposed scheme utilises one cycle post-fault current signals of all the lines as an input to SVM. In order to achieve the most optimised classifier, Gaussian radial basis function has been used for training of SVM. Feasibility of the proposed scheme has been tested by modelling an existing 400 kV Indian busbar system in PSCAD/EMTDC software package. More than 28 800 fault cases with varying fault resistances, fault inception angles, fault locations, types of faults and source impedances have been generated and used for validation of the proposed scheme. The proposed scheme effectively discriminates between in-zone and out-of-zone faults with very high fault classification accuracy for different fault and system conditions. Moreover, the proposed scheme remains stable during an early and severe current transformer (CT) saturation condition giving an accuracy of 99% for all the fault cases.

Inspec keywords: Gaussian processes; busbars; current transformers; radial basis function networks; support vector machines; power engineering computing; fault location

Other keywords: varying fault resistances; SVM; fault zone identification; fault inception angles; fault locations; support vector machine; Gaussian radial basis function; busbar; current transformer saturation; PSCAD/EMTDC software package

Subjects: Neural computing techniques; Other topics in statistics; Knowledge engineering techniques; Power engineering computing; Power system protection; Transformers and reactors; Other topics in statistics; Inspection and quality control

References

    1. 1)
      • N. Cristianini , J. Shawe-Taylor . (2000) An introduction to support vector machines and other kernel-based learning methods.
    2. 2)
      • B.A. Oza , N.C. Nair , R.P. Mehta , V.H. Makwana . (2009) Power System protection and switchgear.
    3. 3)
    4. 4)
      • M.R. Aghaebrahimi , H. Khorashadi Zadeh . Fuzzy neuro approach to busbar protection: design and implementation. Int. J. Inf. Technol. , 1 , 66 - 70
    5. 5)
      • Y.G. Paithankar , S.R. Bhide . (2010) Fundamentals of power system protection.
    6. 6)
      • C.W. Hsu , C.C. Chang , C.J. Lin . (2003) A practical guide to support vector classification.
    7. 7)
    8. 8)
      • Jiang, F., Bo, Z.Q., Redfern, M.A., Weller, G., Chen, Z., Xinzhou, D.: `Application of wavelet transform in transient protection-case study: Busbar protection', Proc. Seventh Int. Conf. on Developments in Power System Protection, April 2001, p. 197–200.
    9. 9)
    10. 10)
    11. 11)
    12. 12)
    13. 13)
      • Zadeh, H.K.: `Fuzzy neuro approach to busbar protection', IEEE Power Engineering Society General Meeting, 12–16 June 2005, 2, p. 1089–1093.
    14. 14)
    15. 15)
      • H.-T. Lin . Using (lib) SVM Tutorial.
    16. 16)
      • Chen, Z., Bo, Z.Q., Lin, X.-n., Caunce, B.R.J.: `Integrated line and busbar protection scheme based on wavelet analysis of fault generated transient current signals', Int. Conf. on Power System Technology – POWERCON 2004 Singapore, 21–24 November 2004, 1, p. 396–401.
    17. 17)
    18. 18)
    19. 19)
      • ‘PSCAD/EMTDC Manual’, Getting Started, Winnipeg, Manitoba Canada, Manitoba HVDC Research Centre Inc., January 2001.
    20. 20)
      • C.-C. Chang , C.-J. Lin . LIBSVM-A Library for Support Vector Machines.
    21. 21)
      • Feser, K., Braun, U., Engler, F., Maier, A.: `Application of neural networks in numerical busbar protection systems (NBPS)', Proc. First Int. Forum on Applications of Neural Networks to Power Systems, 23–26 July 1991, p. 117–121.
    22. 22)
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