Journal of the Korean Institute of Illuminating and Electrical Installation Engineers (조명전기설비학회논문지)

The Korean Institute of IIIuminating and Electrical Installation Engineers (한국조명전기설비학회)
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Development of Artificial-Intelligent Power Quality Diagnosis Algorithm using DSP

DSP를 이용한 인공지능형 전력품질 진단기법 연구

  • Published : 2009.01.31
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Abstract

This paper proposes a new Artificial-Intelligent(AI) Power Quality(PQ) diagnosis algorithm using Discrete Wavelet Transform(DWT), Fast Fourier Transform(FFT), Root-Mean-Square(RMS) value. The developed algorithm is able to detect and classify the PQ problems such as the transient, the voltage sag, the voltage swell, the voltage interruption and the total harmonics distortion. The 15.36[kHz] sampling frequency is used to measure the voltages in a power system. The measured signals are used for DWT, FFT, RMS calculation. For AI diagnosis of the PQ problems, a simple multi-layered Artificial Neural Network(ANN) with the back-propagation algorithm is adopted, programmed in C++ and tested in PSIM simulation studies. Finally, the algorithm, which is installed in MP PQ+256 with TI DSP320C6713, is proved to diagnose the PQ problems efficiently.

본 논문은 이산웨이블렛 변환, 푸리에 변환 및 실효값의 연산 결과를 이용하여 전력품질을 진단하는 인공지능형 진단기법을 제안한다. 제안된 진단기법을 채택한 인공지능형 전력품질 진단기는 과도현상, 순간전압강하, 순간전압상승, 순간정전 및 전고조파 외형률의 진단 및 분류가 가능하다. 신호처리를 위한 데이터 샘플링주파수는 15.36[kHz]가 사용되었으며 샘플링된 이산데이터는 이산웨이블렛변환, 고속푸리에변환, 실효값의 연산에 사용되어진다. 효율적인 인공지능형 전력품질 진단을 위해서, 진단하고자 하는 전력품질 요소에 맞추어 간단한 다층구조의 인공신경망을 설계하였다. 제안된 인공신경망은 C++ 언어로 프로그램되어 PSIM 시뮬레이션 연구에 사용되었으며, TI DSP 320C6713 마이크로프로세서를 사용한 MP PQ+256 하드웨어에서 검증하였다.

Keywords

References

  1. G.B. Chung "중앙 집중형 전력계통에서의 전력품질 규제" 전력전자학회, 전력전자학회지 제10권 제6호, pp. 26-32 Dec. 2005
  2. Wei-Ming Tong1, Xue-Lei Song1, Dong-Zhong Zhang2 "Recognition and Classification of Power Quality Disturbances Based on Self-adaptive Wavelet Neural Network" - ISNN 2006, pg 1386- 1394, May 2006
  3. Liu Hu`a, Wang Yuguo, Zhao Wei, "Power Quality Disturbances Detection and Classification Using Complex Wavelet Transformation and Artificial Neural Network" Control Conference, 2007. CCC 2007. Chinese, pg 208-212, June 2007
  4. J.H. Choi, H.K. Kim, J.M. Lee, G.B. Chung "Power Disturbance Classifier Using Wavelet-Based Neural Network," KIPE, Journal of Power Electronic(JPE), Vol.6, No.4, pp.307-314, Oct. 2006
  5. Anis Ibrahim, W.R. Morcos, M.M. "artificial intelligence and advanced mathematical tools for power quality applications : A Survey" Power Delivery, IEEE Transactions on, Vol 17, pg 668-673, Apr 2002 https://doi.org/10.1109/61.997958
  6. A. Serdar Yilmaz, Abdulhamit Subasi, Mehmet Bayrak, Vedat M. Karsli, Ergun Ercelebi "Application of lifting based wavelet transforms to characterize power quality events" Energy Conversion and Management 48, pg 112-123 2007 https://doi.org/10.1016/j.enconman.2006.05.003
  7. Gaouda, A.M. Salama, M.M.A. Sultan, M.R. Chikhani, A.Y. "Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition" Power Delivery, IEEE Transactions on, Vol 14, pg 1469-1476, Oct 1999 https://doi.org/10.1109/61.796242
  8. Godoy, R.B. Pinto, J.O.P. Galotto, L. "Multiple signal processing techniques based power quality disturbance detection, classification, and diagnostic software" Electrical Power Quality and Utilisation, 2007. EPQU 2007. 9th International Conference on, pg 1-6, Oct 2007
  9. "IEEE Recommended Practice for Monitoring Electric Power Quality," IEEE standards Coordinating Committee 22, June. 1995
  10. Gaouda, A.M. Salama, M.M.A. Sultan, M.R. Chikhani, A.Y. "Power Quality Detection and Classification Using Wavelet-Multiresolution Signal Decomposition" Power Delivery, IEEE Transactions on, Vol 14, pg 1469-1476, Oct 1999 https://doi.org/10.1109/61.796242
  11. Monedero, I, Leon, C, Ropero, J, Garcia, A, Elena, J.M, Montano, J.C. "Classification of Electrical Disturbances in Real Time Using Neural Networks" Power Delivery, IEEE Transactions on, Vol 22, pg 1288-1296, July 2007 https://doi.org/10.1109/TPWRD.2007.899522
  12. Kasula, V.D.K. "Classification of Power Quality Disturbance Signals Using FFT, STFT, Wavelet Transforms and Neural Networks - A Comparative Analysis" Conference on Computational Intelligence and Multimedia Applications, 2007. International Conference on, Vol 1, pg 335-340, Dec 2007
  13. Santoso, S. Powers, E.J.Grady, W.M. Parsons, A.C. "power quality disturbance waveform recognition using wavelet-based neural classifier -part 1: theoreticafoundation" Power Delivery, IEEE Transactions on, Vol 15, pg 222-228, Jan 2000 https://doi.org/10.1109/61.847255
  14. P. L. Mao and R. K. Aggarwa, "A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network", IEEE Trans.Power Delivery, Issue: 4, Vol. 16, pp. 654 660, 2001, Oct https://doi.org/10.1109/61.956753
  15. 정슬 "신경회로망의 구조 및 사용법," 충남대학교 출판부, Aug. 2004-

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