Feasibility of spectral analysis techniques for disruption analysis in Aditya tokamak

  • Authors

    • T Thaj Mary Delsy Sathyabama Institute of Science and Technology
    • N M. Nandhitha Sathyabama Institute of Science and Technology
    • B Sheela Rani Sathyabama Institute of Science and Technology
    2018-12-05
    https://doi.org/10.14419/ijet.v7i4.19396
  • Biorthogonal Wavelet, Tokamak, Diagnostic Signals, Skewness, Sensitivity.

  • Abstract

    Aditya Tokamak is a medium size fusion reactor that uses plasma for the generation of power. Magnetic fields are used to confine plasma inside the torus. Release of plasma from its confinement is called plasma disruption. Plasma disruption is a dangerous event, which damages the in – vessel components of the Tokamak. So the early stage prediction of plasma disruption is quite important. Wavelet transform is a powerful tool for the analysis of the non - stationary signals. In this paper, analysis of plasma disruption signals using Biorthogonal wavelet transforms is perform to identify disruption. Plasma current, Vloop, Halpha, Hard X ray, Mirnov coil signal, Soft X-ray are diagnostic signals. Performance is measured in terms of sensitivity and specificity.

     

     

  • References

    1. [1] Bhooshan Paradkar, J. Ghosh, P. K. Chattopadhyay, R. L. Tanna, D. Raju, S. B. Bhatt, C. V. S. Rao, Sankar Joisa, Santanu Banerjee, R. Manchanda, C. N. Gupta, Y. C. Saxena, and Aditya Team, Runaway –loss induced negative and positive loop voltage spikes in the Aditya Tokamak Physics of Plasma, 2010, Vol.17, Issue 9, doi.org/10.1063/1.3474949.

      [2] K. Sudheera and N. M. Nandhitha , Application of Hilbert Transform for Flaw Characterization in Ultrasonic Signals, Indian Journal of Science and Technology, , DOI: 10.17485/ijst/2015/v8i13/56303, July 2015, Vol 8(13), pp.1-6 https://doi.org/10.17485/ijst/2015/v8i13/56303.

      [3] Jose Albin, A & N.M., Nandhitha, Text independent human voice ranking system for audio search engines using wavelet features, ARPN Journal of Engineering and Applied Sciences, 2015, 10, pp.1650-1653.

      [4] T. T. M. Delsy, N. M. Nandhitha, R. L. Tanna and J. Ghosh, "Spectral statistical analysis of low frequency coefficients from diagnostic signals depicting MHD disruptions," 2017 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Kollam, 2017, 978.- 1- 5090-4967- 7/17/$31.00 © 2017 IEEE. https://doi.org/10.1109/ICCPCT.2017.8074353.

      [5] Selvarasu, N & Nachiappan, A & N.M., Nandhitha, Effective representation of Non-Uniformity and asymmetry in breast thermographs using statistical parameters on histograms of Wavelet Coefficients for cancer detection. European Journal of Scientific Research.2012, 80, pp.10-19.

      [6] R L Tanna, J Ghosh, Harshita Raj, Rohit Kumar, Suman Aich, Vaibhav Ranjan, K A Jadeja, K M Patel, S B Bhatt, K Sathyanarayana1, Production And Preliminary Results From The Aditya Upgrade Tokamak, Plasma Science and Technology, May 2018 , Vol.20, Number 7.

      [7] Binh Nguyen, Dang Nguyen, Wanli Ma, and Dat Tran, Wavelet transform and adaptive arithmetic coding techniques for EEG lossy compression, 978-1-5090-6182-2/17/$31.00 ©2017 IEEE, pp.3153-3160.

      [8] Xiaojiao Chen, Liansheng Huang, Peng Fu, Ge Gao, Shiying He, Jun Shen, Lili Zhu, and Lin Dong , ‘The Design on the Real-Time Wavelet Filter for ITER PF AC/DC Converter Control System’, IEEE Transactions on Plasma Science, Vol. 44, No. 7, 2016, pp1178 - 1186. https://doi.org/10.1109/TPS.2016.2572200.

      [9] C. Vimala and P. A. Priya, “Noise reduction based on double density discrete wavelet transform,†in Proc. Int. Conf. Smart Struct. Syst. (ICSSS), Oct. 2014, pp. 15–18. https://doi.org/10.1109/ICSSS.2014.7006177.

      [10] SE Roslin, NM Nandhitha, CCJ Rekha, A Monisha,Node state information based adaptive power allocation and energy efficient routing using fuzzy logic for wireless sensor network, Int J Appl Eng Res 9 (21), pp.8575-92.

      [11] Baochen Jiang, Aiping Yang, Chengyou Wang and Zhengxin Hou, Implementation of Biorthogonal Wavelet Transform Using Discrete Cosine Sequency Filter International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 6, No. 4, August, 2013, pp 179 – 189.

      [12] H. O. Mota, N. D. O. Volpini, G. F. Rodrigues , F. H. Vasconcelos ,A real-time processing system for denoising of partial discharge signals using the wavelet transform, 978-1-4244-2092-6/08/$25.00 ©2008 IEEE,pp 391-395.

      [13] K. Yoshimatsu, N. Okamoto, Y. Kawahara, K. Schneider and M. Farge. ‘Coherent vorticity and current density simulation of three-dimensional magnetohydrodynamic turbulence using orthogonal wavelets’. Geophysical and Astrophysical Fluid Dynamics, 2013, 107(1-2), pp.73-80. https://doi.org/10.1080/03091929.2012.654790.

      [14] B.B.S.Kumar, Dr.P.S.Satyanarayana, ‘Image Analysis Using Biorthogonal Wavelet’, International Journal Of Innovative Research & Development, June, 2013 Vol 2 Issue 6, pp.543 – 565.

      [15] Zhang, H., Blackburn, T. R. et al. “A novel wavelet transform technique for on-line partial discharge measurements – part 1: WT de-noising algorithm.†IEEE Transactions on Dielectrics and Electrical Insulation. Frebruary 2007 Vol. 14, no. 1.

      [16] P. Fu et al., “Review and analysis of the AC/DC converter of ITER coil power supply,†in Proc. 25th Annu. IEEE Appl. Power Electron. Conf.Expo. (APEC), Feb. 2010, pp. 1810–1816. https://doi.org/10.1109/APEC.2010.5433479.

      [17] R. W. Schafer, “What is a Savitzky–Golay filter?†IEEE Signal Process. Mag., Jul. 2011, Vol. 28, no. 4, pp. 111–117. https://doi.org/10.1109/MSP.2011.941097.

      [18] Q. Gao, Y. Lu, D. Sun, and D. Zhang, “A multiscale products technique for denoising of dna capillary electrophoresis signals,†Meas. Sci. Technol., 2013, Vol. 24, no. 6, 2013, https://doi.org/10.1088/0957-0233/24/6/065004.

      [19] K. Hallatschek and M. Zilker, “Real time data acquisition with transputers and PowerPCs using the wavelet transform for event detection,†IEEE Trans. Nucl. Sci., Aug. 1998, Vol. 45, no. 4, pp. 1872–1876. https://doi.org/10.1109/23.710954.

      [20] J. Gruber, Z. Sekeresova, J. Hlina, and J. Sonsky, “Analysis of thermal plasma dynamics in 3-D using tomographical reconstruction and wavelet analysis,†IEEE Trans. Plasma Sci., Nov. 2011, Vol. 39, no. 11, pp. 2850–2851. https://doi.org/10.1109/TPS.2011.2155093.

      [21] Y. Hongwen et al., “On the sequential control of ITER poloidal field converters for reactive power reduction,†Plasma Sci. Technol., 2014, Vol. 16, no. 12, p.p.1147. https://doi.org/10.1088/1009-0630/16/12/11.

  • Downloads

    Additional Files

  • How to Cite

    Thaj Mary Delsy, T., M. Nandhitha, N., & Sheela Rani, B. (2018). Feasibility of spectral analysis techniques for disruption analysis in Aditya tokamak. International Journal of Engineering & Technology, 7(4), 3843-3846. https://doi.org/10.14419/ijet.v7i4.19396

    Received date: 2018-09-10

    Accepted date: 2018-10-09

    Published date: 2018-12-05