Detection of Power Quality Disturbances Based on Adaptive Neural Net and Shannon Entropy Method

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Detection of power quality (PQ) events is a vital task for the power system monitoring and control. This paper presents a new scheme for the revealing of PQ disturbances using adaptive neural net (ANN) and information theory which employs neural net as a harmonics extracting unit and the difference entropy as a feature extracting unit. Simulations on six signals, such as ideal sine wave, interruption, voltage sag, voltage swell, impulse, and oscillation transient, are done with and without the presence of harmonics and the begin and end instants of disturbances are accurately tracked. The robust nature of the algorithm allows accurate estimation in the presence of noises about 10 db and the results of detection show that the proposed method has good compliance on determination of attributes of the signals.


Neural network Difference entropy Power quality Detection Harmonics 


  1. 1.
    Z. Jing, H. Zhengyou, Q. Qingquan, Detection of power quality disturbances based on generalized morphological filter and information theory. IEEE. (2009)Google Scholar
  2. 2.
    Yop Chung, Dong-Jun Won, Joong-Moon Kim et al., Development of a network-based power quality diagnosis system. Electr. Power Syst. Res. 77, 1086–1094 (2007)CrossRefGoogle Scholar
  3. 3.
    A.S. Yilmaz, A. Subasi, M. Bayrak et al., Application of lifting based wavelet transforms to characterize power quality events. Energy Convers. Manage. 48(1), 112–123 (2007)CrossRefGoogle Scholar
  4. 4.
    F. Jurado, J.R. Saenz, Comparison between discrete STFT and wavelets for the analysis of power quality events. Electr. Power Syst. Res. 62(3), 183–190 (2002)CrossRefGoogle Scholar
  5. 5.
    S. Ouyang, J. Wang, A new morphology method for enhancing power quality monitoring system. Int. J. Electr. Power Energy Syst. 29(2), 121–128 (2007)CrossRefGoogle Scholar
  6. 6.
    V. Suresh Kumar, D. Kavitha, K. Kalaiselvi, P.S. Kannan, Harmonic Mitigation and Power Factor Improvement using Fuzzy Logic and Neural Network Controlled Active Power Filter. Journal of Electrical Engineering & Technology. 3(3), 520–527 (2008)Google Scholar
  7. 7.
    D. Kavitha, A.F. Zobaa, P. Renuga, V. Suresh Kumar, NSGA-II optimized neural network controlled active power line conditioner under non-sinusoidal conditions. Int. Rev. Electr. Eng. (IREE). 6(2011)Google Scholar
  8. 8.
    V. Suresh Kumar, D. Kavitha, K. Kalaiselvi, P.S. Kannan, Optimal estimation of harmonics in power system using intelligent computing techniques. Proceedings of IEEE Neural Networks conference. (2007)Google Scholar
  9. 9.
    Catarina Moreira, A. Wichert, Finding academic experts on a multisensor approach using Shannon’s entropy. Expert Syst. Appl. 40(14), 5740–5754 (2013)CrossRefGoogle Scholar
  10. 10.
    YuequanBao HuiLi, J. Ou, Structural damage identification based on integration of information fusion and shannon entropy. Mech. Syst. Signal Process. 22, 1427–1440 (2008)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Electrical and Electronics EngineeringThiagarajar College of EngineeringMaduraiIndia

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