International Journal of Fuzzy Logic and Intelligent Systems

  • 제5권4호
  • /
  • Pages.347-352
  • /
  • 2005
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Pattern Classification of Partial Discharge Data

  • Kim Sung-Ho (School of Electronics and Information Engineering, College of Engineering Kunsan National University) ;
  • Bae Geum-Dong (School of Electronics and Information Engineering, College of Engineering Kunsan National University)
  • 발행 : 2005.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

PD(Partial discharges) are small electrical sparks that occur within the electric insulation of cables, transformers and windings on motors. PD analysis is a proactive diagnostic approach that uses PD measurements to evaluate the integrity of this equipment. Recently, several diagnostic algorithms for classifying the type of PD and locating the defect position have been developed. In this work, a new PD recognition system is proposed, which utilizes approximate coefficients of wavelet transform as a feature vector, furthermore, introduces bank of Elman networks to recognize the various PD phenomena. In order to verify the performance of the proposed scheme, it is applied to the simulated PD data.

키워드

참고문헌

  1. C. Chang and Q. SU, 'Partial discharge measurement and analysis using an intergrated system', Center for Electrial Power Engineering, Monash University, Clayton VIC 3168 Austrailia
  2. M. de Nigris et al., 'Cable diagnosis based on defect location and characterization through partial discharge measurements', http://www.cigre.org, CIGRE Session 2002, 15-109, 2002
  3. Park, Sung Hee, 'Discrimination of Complex Partial Discharge Sources by Neural Networks', Dept. Electrical Engineering, Graduate School, Chunbuk National University, Cheonju, Korea, 2004
  4. R.Bartnikas, 'Partial Discharges Their mechanism. Detection and measurement', IEEE,Trans.on EL. Vol. 9. No.5, p.763-778, 2002
  5. G. Paoletti and A. Golubev, 'Partial Discharge Theory and Applications to Electrical Systems', the 1999 IEEE lAS Pulp and Paper Industry Conference, Seattle, 1999
  6. wavelet algorithm Wavelet Toolbox: Advanced Concepts: Algorithms, software 'matlab6.5' help document
  7. Brani Vidakovic, 'Statistical Modeling by Wavelets', Wileyseries in probability and statistics, 1999
  8. Howard L. Resnikoff and Ramond O. wells, Jr. 'Wavelet analysis' The scalable structure of infonnation', Springer, 1998
  9. elman network http://www.cs.wright.edu/~mrizki /cs765/Lectures /Lecture12.ppt#315, 24, Elman Algorithm
  10. T. Lane and C.E. Brodley, 'An application of machine learning to anomaly detection.', In Proceedings of the 20th National Information Systems Security Conference, p.366{377, October 1997. 1998 IEEE Computer Society, IEEE Computer Society Press. Scottsdale, AZ
  11. T. J. Cholewo and J. M. Zurada, 'Sequential Network Construction for Time Series Prediction', IEEE international joint Conference on Neural Networks, p.2034-2039, Houston, Texas, USA, June 9-12, 1997 https://doi.org/10.1109/ICNN.1997.614214