Chun-Xia Dou, Ting Gui, Ye-Fei Bi, Jin-Zhao Yang and Xiao-Gang Li. Assessment of Power Quality Based on D-S Evidence Theory. International Journal of Automation and Computing, vol. 11, no. 6, pp. 635-643, 2014. https://doi.org/10.1007/s11633-014-0837-y
Citation: Chun-Xia Dou, Ting Gui, Ye-Fei Bi, Jin-Zhao Yang and Xiao-Gang Li. Assessment of Power Quality Based on D-S Evidence Theory. International Journal of Automation and Computing, vol. 11, no. 6, pp. 635-643, 2014. https://doi.org/10.1007/s11633-014-0837-y

Assessment of Power Quality Based on D-S Evidence Theory

doi: 10.1007/s11633-014-0837-y
Funds:

This work was supported by National Natural Science Foundation of China (No. 51177142) and Natural Science Foundation of Hebei Province (No. F2012203063)

  • Received Date: 2012-09-26
  • Rev Recd Date: 2013-11-21
  • Publish Date: 2014-12-20
  • Technological advancement of measurement systems has enhanced the accuracy of power quality assessment by using a combination of measured information. This paper proposes a novel approach for estimating power quality based on information fusion technique of Dempster-Shafer (D-S) evidence theory. First, in order to accurately extract transient features regarding power quality indexes, wavelet packet transform and lifting wavelet transform are proposed to detect various disturbance signals' measurement. By using many kinds of transformed transient indexes and steady state indexes, a novel reliability distribution function is constructed, and synthesized assessment index of power quality is drafted based on information fusion technique of D-S evidence theory. Finally, the simulation results prove that D-S evidence theory is a more effective means for evaluating the power quality.

     

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  • [1]
    K. R. Krishnanand, S. K. Nayak, B. K. Panigrahi, V. R. Pandi, P. Dash. Classification of power quality disturbances using GA based optimal feature setion. In Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence, Lecture Notes in Computer Science, Springer-Verlag, New Delhi, India, vol. 5909, pp. 561-566, 2009.
    [2]
    S. Kaewarsa, K. Attakitmongcol, W. Krongkitsiri.Waveletbased intelligent system for recognition of power quality disturbance signals. In Proceedings of the 3rd International Symposium on Neural Networks, Lecture Notes in Computer Science, Springer, Chengdu, China, vol. 3972, pp. 1378-1385, 2006.
    [3]
    A. Kumar, S. K. Choi, L. Goksel. Tolerance allocation of assemblies using fuzzy comprehensive evaluation and decision support process. International Journal of Advanced Manufacturing Technology, vol. 5, no. 1-4, pp. 379-391, 2011.
    [4]
    M. Kowal, J. Korbicz. Fault detection under fuzzy model uncertainty. International Journal of Automation and Computing, vol. 4, no. 2, pp. 117-124, 2003.
    [5]
    H. M. Liu, F. Qu, X. Y. Chen, G. Y. Xue. Comprehensive assessment of power quality based on the model tree. Power Demand Side Management, vol. 10, no. 3, pp. 19-23, 2008. (in Chinese)
    [6]
    O. Leila, E. Noor, B. Ahmad, A. Azuraliza, B. Khairul, N. Maulud. An expert system applied in storm water management plan for construction sites in Malaysia. Expert Systems with Application, vol. 39, no. 3, pp. 3692-3701, 2012.
    [7]
    X. Chen, T. Limchimchol. Monitoring grinding wheel redress-life using support vector machines. International Journal of Automation and Computing, vol. 3, no. 1, pp. 56-62, 2006.
    [8]
    B. H. Fang, Z. H. Cheng, H. P. Liu. Application of projection pursuit model in integrated evaluation of national economy. Operations Research and Management Science, vol. 14, no. 5, pp. 85-88, 2005. (in Chinese)
    [9]
    J. Wiley, S. Ltd. Handbook of Power Quality, Italy: Angelo Baggini University of Bergamo, pp. 631-644, 2008.
    [10]
    L. Chen, Y. H. Xu. Discussion about the methods of evaluating power quality. North China Electric Power University, vol. 24, no. 1, pp. 58-61, 2005. (in Chinese)
    [11]
    L. Zhou, Q. H. Li, F. Zhang. Application of genetic projection pursuit interpolation model on power quality synthetic evaluation. Power System Technology, vol. 31, no. 7, pp. 32-35, 2007. (in Chinese)
    [12]
    W. G. Morsi, M. E. El-Hawary. Wavelet packet transformbased power quality indices for balanced and unbalanced three-phase systems under stationary or nonstationary operating conditions. IEEE Transactions on Power Delivery, vol. 24, no. 4, pp. 2300-2310, 2009.
    [13]
    A. M. Gaouda, M. M. A. Salaam, M. R. Sultan, A. Y. Chikhani. Power quality detection and classification using wavelet multi-resolution signal decomposition. IEEE Transactions on Power Delivery, vol. 14, no. 4, pp. 1469-1476, 1999.
    [14]
    L. H. Wang, S. Y. Yang, R. H. Du. Selection and application of mother wavelet in the analysis of transient signals. China Power, vol. 41, no. 10, pp. 27-29, 2008. (in Chinese)
    [15]
    H. Liu, L. P. Zhai, Y. Gao, W. M. Li, J. F. Zhou. Image compression based on biorthogonal wavelet transform. In Proceedings of IEEE International Symposium on Communications and Information Technology, IEEE, Beijing, China, vol. 1, pp. 598-601, 2005.
    [16]
    X. B. Guo, Q. X. Gao. Research of voltage transient disturbance detection based on wavelet transform. Microcomputer Information, vol. 25, no. 3, pp. 208-209, 2009. (in Chinese)
    [17]
    J. P. Qi, W. F. Yuan. Detection algorithm of transient power quality disturbance based on wavelet transformation. Journal of Shenzhen Institute of Information Technology, vol. 9, no. 1, pp. 69-71, 2011. (in Chinese)
    [18]
    H. Liu, G. H. Liu, Y. Sheng. A novel real time harmonic detection method using fast lifting wavelet transform. Journal of Jiangsu University (Natural Science Edition), vol. 30, no. 3, pp. 288-291, 2009. (in Chinese)
    [19]
    P. K. Parlewar, K. M. Bhurchandi. A 4-quadrant curvelet transform for denoising digital images. International Journal of Automation and Computing, vol. 10, no. 3, pp. 217-226, 2013.
    [20]
    W. Liu. Wideband beamforming for multipath signals based on frequency invariant transformation. International Journal of Computer Applications, vol. 9, no. 4, pp. 420-428, 2012.
    [21]
    W. Sweldens. The lifting scheme: A custom-design construction of biorthogonal wavelets. Applied and Computational Harmonic Analysis, vol. 3, no. 2, pp. 186-200, 1996.
    [22]
    G.Quellec, M.Lamard, G.Cazuguel, B.Cochener, C.Roux. Adaptive non-separable wavelet transform via lifting and its application to content-based image retrieval. IEEE Transactions on Image Processing, vol. 19, no. 1, pp. 25-35, 2010.
    [23]
    S. J. Yuan. Application of db4 wavelet in power network fault detection. Microcomputer Information, vol. 27, vol. 6, pp. 51-52, 2011. (in Chinese)
    [24]
    Q. Q. Jia. Study of earth fault detection for power distribution networks based on D-S evidence theory. China Power, vol. 40, no. 1, pp. 28-31, 2007. (in Chinese)
    [25]
    M. Shoyaib, M. Abdullah-Al-Wadud, O. Chae. A reliable skin detection using Dempster-Shafer theory of evidence. In Proceedings of International Conference on Computational Science and Its Applications, Lecture Notes in Computer Science, Springer, Seoul, Korea, vol. 5593, pp. 764-779, 2009.
    [26]
    K. H. Guo, W. L. Li. Combination rule of D-S evidence theory based on the strategy of cross merging between evidences. Expert Systems with Applications, vol. 38, no. 10, pp. 13360-13366, 2011. (in Chinese)
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