Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

Application of a modified wavelet threshold denoising algorithm in system identification of WPTS

  • Qiming Huang (School of Electrical Engineering and Automation, Wuhan University) ;
  • Qijun Deng (School of Electrical Engineering and Automation, Wuhan University) ;
  • Zhifan Li (School of Electrical Engineering and Automation, Wuhan University) ;
  • Peng Luo (School of Electrical Engineering and Automation, Wuhan University)
  • Received : 2023.10.30
  • Accepted : 2024.02.21
  • Published : 2024.07.20
⟨ Previous Next ⟩

Abstract

System identification is an effective method to model a wireless power transfer system when it is used for wireless charging of electric vehicles. However, system identification using raw data directly is often unsatisfactory due to the inevitable noise interference from system operation and signal acquisition. This study proposes an improved wavelet threshold denoising (WTD) algorithm with optimized algorithm parameters and design methods. First, the number of decomposition layers is determined based on the signal spectrum diagram. Second, adaptive thresholds are designed for different decomposition layers. Third, the hierarchical threshold is combined with the hardness adjustable threshold function. Last, recursive least squares is employed to obtain a system model with the data denoised by the proposed method. Experiments demonstrate that the improved WTD method increases the accuracy of system identification to 85.42%, which verifies the effectiveness of the proposed method. An internal model controller is also designed based on the obtained model.

Keywords

Acknowledgement

This work was supported in part by the National Natural Science Foundation of China under Grant 51977151 and in part by the Fundamental Research Funds for the Central Universities under Grant 2042021gf0011.

References

  1. Deng, Q., Li, Z., Liu, J., Li, S., Luo, P., Cui, K.: Data-driven modeling and control considering time delays for WPT system. IEEE Trans. Power Electron. 37(8), 9923-9932 (2022)
  2. Li, Z., et al.: Receding horizon D-optimal input design for identification of wireless power transfer systems. IEEE J. Emerg. Select. Top. Power Electron. 11(3), 3597-3606 (2023)
  3. Bultan, A., Haddad, R. A.: System identification with denoising. In: 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), vol. 1, pp 576-579 (2000)
  4. Hao, F., Shiming, Y., Xingang, W., Shoujue, W.: System identification method with denoising and disturbance-rejecting capability. In: 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering. TENCOM'02. Proceedings, vol 3, pp. 1269-1272 (2002)
  5. Huang, N.E., et al.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, (1998)
  6. Chang, S.Y., Wu, H.-C.: Tensor wiener filter. IEEE Trans. Signal Process. 70, 410-422 (2022)
  7. Zhang, X., Jiang, S.: Application of Fourier transform and Butterworth filter in signal denoising. In: 2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP), pp. 1277-1281 (2021)
  8. Durand, S., Froment, J.: Artifact free signal denoising with wavelets. In: 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), vol. 6, pp. 3685-3688 (2001)
  9. Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 11(7), 674-693 (1989)
  10. Daubechies, I.: The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. Inf. Theory 36(5), 961-1005 (1990)
  11. Shiguo, C., Ruanyu, Z., Peng, W., Taihua, Li.: Enhance accuracy in pole identification of system by wavelet transform de-noising. IEEE Trans. Nucl. Sci.Nucl. Sci. 51(1), 250-255 (2004)
  12. Li, Y., Wei, H.-L., Billings, S. A.: Identification of time-varying systems using multi-wavelet basis functions. In: IEEE Transactions on Control Systems Technology, vol. 19, no. 3, pp. 656-663, May (2011)
  13. Srivastava, M., Anderson, C.L., Freed, J.H.: A new wavelet denoising method for selecting decomposition layers and noise thresholds. IEEE Access 4, 3862-3877 (2016)
  14. Gu, J.: Wavelet threshold de-noising of power quality signals. Fifth Int. Conf. Nat. Comput. 2009, 591-597 (2009)
  15. Sun, Z., Lu, J.: An ultrasonic signal denoising method for EMU wheel trackside fault diagnosis system based on improved threshold function. IEEE Access 9, 96244-96256 (2021)
  16. Yu, S., Qin, Y., Gao, J., Hou, S., Lyu, F., Li, X.: Performance improvement of wavelet noise reduction based on new threshold function. In: 2020 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), pp. 80-84 (2020)
  17. Golroudbari, M. A.: Signal denoising based on wavelet transform using a multi-layer threshold function. In: 2013 7th International Conference on Application of Information and Communication Technologies, pp 1-5 (2013)
  18. Chen, F., Garnier, H., Deng, Q., Kazimierczuk, M.K., Zhuan, X.: Control-oriented modeling of wireless power transfer systems with phase-shift control. IEEE Trans. Power Electron. 35(2), 2119-2134 (2020)
  19. Czarkowski, D., Kazimierczuk, M.K.: Phase-controlled seriesparallel resonant converter. IEEE Trans. Power Electron. 8(3), 309-319 (1993)
  20. Tian, M., Wen, H., Zhou, L., You, X.: Image denoising using multi-scale thresholds method in the wavelet domain. Int. Conf. Wavelet Anal. Pattern Recogn. 2010, 79-83 (2010)
  21. John, A., Sadasivan, J., Seelamantula, C.S.: Adaptive Savitzky-Golay filtering in non-Gaussian noise. IEEE Trans. Signal Process. 69, 5021-5036 (2021)
  22. Ossareh, H. R., Wisotzki, S., Buckland Seeds, J., Jankovic, M.: An internal model control-based approach for characterization and controller tuning of turbocharged gasoline engines. In: IEEE Transactions on Control Systems Technology, vol. 29, no. 2, pp. 866-875, (2021)