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Fault Data Compression of Power System with Wavelet Neural Network Based on Wavelet Entropy

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

Through the analysis of function approximation with wavelet transformation, an adaptive wavelet neural network is introduced in the paper, which is applied in data compression of fault data in power system. In addition, the wavelet entropy is adopted to choose the hidden nodes in the wavelet neural network. The learning algorithm of the wavelet neural network based on wavelet entropy is proposed and discussed for data compression of fault data in power system. The simulation results show that it is feasible and valid in the end.

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References

  1. Zhang, Q.H., Benveniste, A.: Wavelet Network. IEEE Trans. on Neural Networks 3(6), 889–898 (1992)

    Article  Google Scholar 

  2. Pati, Y.C., Krishnaprasad, P.S.: Analysis and Synthesis of Feedforward Neural Network Using Discrete Affine Wavelet Transformations. IEEE Trans. on Neural Networks 4(1), 73–85 (1993)

    Article  Google Scholar 

  3. Szu, H.H., Telfer, B., Kadambe, B.: Neural Network Adaptive Wavelets for Signal Representation and Classification. Optical Engineering 31(9), 1907–1916 (1992)

    Article  Google Scholar 

  4. Zhang, J., Walter, G.G., Miao, Y.B.: Wavelet Neural Network for Function Learning. IEEE Trans. on Signal Processing 43(6), 1485–1497 (1995)

    Article  Google Scholar 

  5. Jiao, L.C., Pan, J., Fang, Y.W.: Multiwavelet Neural Network and Its Approximation Properties. IEEE Trans. on Neural Networks 12(5), 1060–1066 (2001)

    Article  Google Scholar 

  6. Zhang, Q.H.: Using Wavelet Networks in Nonparametric Estimation. IEEE Trans. on Neural Networks 8(2), 227–236 (1997)

    Article  Google Scholar 

  7. Delyon, B., Juditsky, A., Benveniste, A.: Accuracy Analysis for Wavelet Approximations. IEEE Trans. on Neural Networks 6(2), 332–348 (1995)

    Article  Google Scholar 

  8. Bakshi, B.R., Stepphanopoulous, B.R.: Wave-net: A Multi-resolution, Hierarchical Neural Network with Location Learning. AIChE Journal 39(1), 57–81 (1993)

    Article  Google Scholar 

  9. Quiroga, R.Q., Rosso, O.A., Basar, E.: Wavelet Entropy in Event-related Potential: A New Method Shows Ordering of EED Oscillations. Biological Cybernetics 84(4), 291–299 (2001)

    Article  MATH  Google Scholar 

  10. Sello, S.: Wavelet Entropy as a Measure of Solar Cycle Complexity. Astron. Astrophys 363(5), 311–315 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Electrification & Automation, Southwest Jiaotong University, Chengdu, Sichuan, 610031, China

    Zhigang Liu & Dabo Zhang

Authors
  1. Zhigang Liu
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  2. Dabo Zhang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, Z., Zhang, D. (2006). Fault Data Compression of Power System with Wavelet Neural Network Based on Wavelet Entropy. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_202

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  • DOI: https://doi.org/10.1007/11760023_202

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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