American Journal of Electrical Power and Energy Systems

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Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique

Received: 05 February 2015    Accepted: 15 February 2015    Published: 02 March 2015
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Abstract

This paper proposes a new technique based on S-transform time-frequency analysis and Fuzzy expert system for classifying power quality (PQ) disturbances. The S-transform is a new time frequency analysis method. It has the features of both continuous wavelet transform (CWT) and short time Fourier transform (STFT). Through S-transform time-frequency analysis, a set of feature components are extracted for identifying PQ disturbances such as; the amplitude of the S-transform matrix and the total harmonic distortion (THD). The two parameters are the inputs to Fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform (e.g. sag, swell, interruption, surge, sag with harmonic and swell with harmonic). Several simulation using Matlab environment and practical data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.

DOI 10.11648/j.epes.20150401.11
Published in American Journal of Electrical Power and Energy Systems (Volume 4, Issue 1, January 2015)
Page(s) 1-9
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Power Quality, S-Transform, Fuzzy Expert System

References
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[3] Y. H. Gu, M. H. J. Bollen,”Time-frequency and time-scale domain analysis of voltage disturbances”, IEEE Trans. Power Deliv., vol. 15, no. 4, pp. 1279–84, 2000.
[4] O. Poisson, P. Rioual, M. Meunier, “Detection and measurement of power quality disturbances using wavelet transform,” IEEE Trans Power Deliv., vol. 15, no. 3, pp. 1039–1044, 2000.
[5] A. M. Gaouda, S. H. Kanoun, M. M. A. Salama, A. Y. Chikhani, “Pattern recognition applications for power system disturbance classification”, IEEE Trans. Power Deliv., vol. 17, no. 3, pp. 677–683, 2002.
[6] Z. L. Gaing, “Wavelet-based neural network for power disturbance recognition and classification,” IEEE Trans. Power Deliv., vol. 19, no. 4, pp. 1560–1568, Oct. 2004.
[7] Abdelazeem A. Abdelsalama, Azza A .Eldesouky, and Abdelhay A. Sallam, “Classification of power system disturbances using linear Kalman filter and Fuzzy-expert system,” Electr. Power Energy Syst., vol. 43, no. 1, pp. 688–695, 2012.
[8] C. I. Chen, G. W. Chang, R. C. Hong, H. M. Li, “Extended real model of Kalman filter for time-varying harmonics estimation,” IEEE Trans Power Deliv., vol. 25, no. 1, pp. 17–26, 2010.
[9] X. Xiao, F. Xu, H. Yang, “Short duration disturbance classifying based on S transform maximum similarity”, Int J Electr Power Energy Syst., vol. 31, no. 7, pp. 374–78, 2009.
[10] S. Suja, Suja Jovitha, “Pattern recognition of power signal disturbances using S Transform and TT Transform,” Int J Electr Power Energy Syst., vol. 32, no. 1, pp. 37–53, 2010.
[11] P. K. Dash, B. K. Panigrahi, G. Panda. “Power quality analysis using s-transform,” IEEE Transactions on Power Delivery, vol. 18, no. 2, pp. 406–411, 2003
[12] S. Santoso, E. J. Powers, W.M. Grady, and A. C.Parsons, “Power quality disturbance waveform recognition using wavelet-based neural classifier -part 2: application,” IEEE Trans. Power Delivery, vol. 15, pp 222-228, Jan. 2000.
[13] Y. Liao, J-B. Lee, “A Fuzzy expert system for classifying power quality disturbances,” International Journal of Electrical Power and Energy Systems, vol. 26, no. 3, pp. 199–205, 2004.
[14] W-M. Lin, C. Wu, C-H. Lin, F. S. Cheng, “Classification of multiple power quality disturbances using support vector machine and one-versus-one approach,” International Conference on Power System Technology, vol. 2, pp. 1–8, 2006.
[15] R. G. Stockwell, L. Mansinha, and R. P. Lowe, “Localization of the complex spectrum: The S-transform,” IEEE Trans. Signal Process., vol. 44, no. 4, pp. 998–1001, Apr. 1996.
[16] K.Passino, S.Yurkovich, Fuzzy Control, Longman: Addison Wesley, 1998.
[17] S. Guo, L. Peter, “A reconfigurable analog Fuzzy logic controller,” Proceeding of the Third IEEE conference on IEEE World congress on computational intelligence, vol. 1, pp. 124-128, June 1994.
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[19] IEEE project group P1159.2. .
Author Information
  • Dept. of Electrical Engineering, El Shorouk Academy, Cairo, Egypt

  • Dept.of Electrical Engineering, Suez Canal University, Ismailia, Egypt

  • Dept. of Electrical Engineering, Port-Said University, Port-Said, Egypt

  • Dept. of Electrical Engineering, Port-Said University, Port-Said, Egypt

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  • APA Style

    Ahmed Hussain Elmetwaly, Abdelazeem Abdallah Abdelsalam, Azza Ahmed Eldessouky, Abdelhay Ahmed Sallam. (2015). Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique. American Journal of Electrical Power and Energy Systems, 4(1), 1-9. https://doi.org/10.11648/j.epes.20150401.11

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    ACS Style

    Ahmed Hussain Elmetwaly; Abdelazeem Abdallah Abdelsalam; Azza Ahmed Eldessouky; Abdelhay Ahmed Sallam. Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique. Am. J. Electr. Power Energy Syst. 2015, 4(1), 1-9. doi: 10.11648/j.epes.20150401.11

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    AMA Style

    Ahmed Hussain Elmetwaly, Abdelazeem Abdallah Abdelsalam, Azza Ahmed Eldessouky, Abdelhay Ahmed Sallam. Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique. Am J Electr Power Energy Syst. 2015;4(1):1-9. doi: 10.11648/j.epes.20150401.11

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  • @article{10.11648/j.epes.20150401.11,
      author = {Ahmed Hussain Elmetwaly and Abdelazeem Abdallah Abdelsalam and Azza Ahmed Eldessouky and Abdelhay Ahmed Sallam},
      title = {Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique},
      journal = {American Journal of Electrical Power and Energy Systems},
      volume = {4},
      number = {1},
      pages = {1-9},
      doi = {10.11648/j.epes.20150401.11},
      url = {https://doi.org/10.11648/j.epes.20150401.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.epes.20150401.11},
      abstract = {This paper proposes a new technique based on S-transform time-frequency analysis and Fuzzy expert system for classifying power quality (PQ) disturbances. The S-transform is a new time frequency analysis method. It has the features of both continuous wavelet transform (CWT) and short time Fourier transform (STFT). Through S-transform time-frequency analysis, a set of feature components are extracted for identifying PQ disturbances such as; the amplitude of the S-transform matrix and the total harmonic distortion (THD). The two parameters are the inputs to Fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform (e.g. sag, swell, interruption, surge, sag with harmonic and swell with harmonic). Several simulation using Matlab environment and practical data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.},
     year = {2015}
    }
    

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  • TY  - JOUR
    T1  - Detection and Identification of PQ Disturbances Using S-Transform and Artificial Intelligent Technique
    AU  - Ahmed Hussain Elmetwaly
    AU  - Abdelazeem Abdallah Abdelsalam
    AU  - Azza Ahmed Eldessouky
    AU  - Abdelhay Ahmed Sallam
    Y1  - 2015/03/02
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    DO  - 10.11648/j.epes.20150401.11
    T2  - American Journal of Electrical Power and Energy Systems
    JF  - American Journal of Electrical Power and Energy Systems
    JO  - American Journal of Electrical Power and Energy Systems
    SP  - 1
    EP  - 9
    PB  - Science Publishing Group
    SN  - 2326-9200
    UR  - https://doi.org/10.11648/j.epes.20150401.11
    AB  - This paper proposes a new technique based on S-transform time-frequency analysis and Fuzzy expert system for classifying power quality (PQ) disturbances. The S-transform is a new time frequency analysis method. It has the features of both continuous wavelet transform (CWT) and short time Fourier transform (STFT). Through S-transform time-frequency analysis, a set of feature components are extracted for identifying PQ disturbances such as; the amplitude of the S-transform matrix and the total harmonic distortion (THD). The two parameters are the inputs to Fuzzy-expert system that uses some rules on these inputs to characterize the PQ events in the captured waveform (e.g. sag, swell, interruption, surge, sag with harmonic and swell with harmonic). Several simulation using Matlab environment and practical data are used to validate the proposed technique. The results depict that the proposed technique has the ability to accurately identify and characterize PQ disturbances.
    VL  - 4
    IS  - 1
    ER  - 

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