Acknowledgement
This work was supported by the Shandong University of Technology and Zibo City Integration Development Project (2019ZBXC011, 2019ZBXC498).
References
- Zhao, F.Z., Yang, R.G.: Voltage sag disturbance detection based on short time Fourier transform. Proc. CSEE. 27(10), 28-34+109 (2007). https://doi.org/10.3321/j.issn:0258-8013.2007.10.005
- Feng, H., Zhou, L.W., Liu, Y.: Detection and classification of transient power quality disturbances based on complex wavelet transform. Power Syst.Technol. 34(03), 91-95 (2010). https://doi.org/10.13335/j.1000-3673.pst.2010.03.004
- Li, P., Gao, J., Chang, D.X.: Hilbert-huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid. J. Modern Power Syst. Clean Energy. 4(01), 19-27 (2016) https://doi.org/10.1007/s40565-016-0188-5
- Huang, N.E., Shen, Z., Long, S.R.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Math. Phys. Eng. Sci. 454(1971), 903-995 (1998) https://doi.org/10.1098/rspa.1998.0193
- Wu, Z.H., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1-41 (2009) https://doi.org/10.1142/S1793536909000047
- Zhang, Y., Liu, Z.G.: Application of EEMD in power quality disturbance detection. Electr. Power Autom. Equip. 31(12), 86-91 (2011). https://doi.org/10.3969/j.issn.1006-6047.2011.12.018
- Bao, Y., Xie, J.: Contrast study on power quality detection using EMD and EEMD. 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), 2074-2077 (2011). https://doi.org/10.1109/CECNET.2011.5768979
- Jin, A.T., Zhuo, F., Mohamed, M.A.: A novel approach based on CEEMDAN to select the faulty feeder in neutral resonant grounded distribution systems. IEEE Trans. Instrum. Meas. 69(7), 4712-4721 (2020). https://doi.org/10.1109/TIM.2019.2954009
- Abdelkader, A.R., Kaddour, A., Bendiabdellah, A., Derouiche, Z.: Rolling bearing fault diagnosis based on an improved denoising method using the complete ensemble empirical mode decomposition and the optimized thresholding operation. IEEE Sens. J. 18(17), 7166-7172 (2018). https://doi.org/10.1109/JSEN.2018.2853136
- Wu, X.Z., Xing, Q., Qu, H., Wang, Q.J., Yang, C.Y.: Application of CEEMD in power quality disturbance detection. Power Syst. Prot. Control. 45(03), 48-55 (2017). https://doi.org/10.7667/PSPC160208
- Zhou, X.L., Tong, X.Y.: Ultra-short-term wind power combined prediction based on CEEMD-SBO-LSSVR. Power Syst. Technol. 45(03), 855-864 (2021). https://doi.org/10.13335/j.1000-3673.pst.2020.0584
- Torres, M. E., Colominas, M. A., Schlotthauer, G., Flandrin, P. A.: complete Ensemble Empirical Mode decomposition with adaptive noise. In: IEEE Int. Conf. on Acoust Speech and Signal Proc. ICASSP-11, Prague, pp. 4144-4147
- Lu, P.Y., Liu, H.X.: Transient power quality detection method based on CEEMDAN-UWT denoising. China Meas. Test. 46(07), 60-67 (2020). https://doi.org/10.11857/j.issn.1674-5124.2019100063
- Colominas, M.A., Schlotthauer, G., Torres, M.E.: Improved complete ensemble EMD: a suitable tool for biomedical signal processing. Biomed. Signal Process. Control 14(1), 19-29 (2014) https://doi.org/10.1016/j.bspc.2014.06.009
- Colominas, M.A., Schlotthauer, G., Torres, M.E.: Noise-assisted EMD methods in action. Adv. Adapt. Data Anal. 04(04), 1250025 (2012) https://doi.org/10.1142/S1793536912500252
- Sun, Y.C., Li, P., Chang, S.J.: EEMD combined with improved PCNN model for noise reduction of gas leakages signa. Comput. Simul. 37(09), 409-414+455 (2020). https://doi.org/10.3969/j.issn.1006-9348.2020.09.086
- Li, J., Li, Q.: Medium term electricity load forecasting based on CEEMDAN-permutation entropy and ESN with leaky integrator neurons. Electr. Mach. Control. 19(8), 70-80 (2015). https://doi.org/10.15938/j.emc.2015.08.011
- Bandt, C., Pompe, B.: Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 88(17), 174102 (2002) https://doi.org/10.1103/PhysRevLett.88.174102
- Zhang, L. L., Wang, H. Y., Wang, W. Q.: A novel harmonic detection method based on CEEMDAN and HT. Electrical Measurement & Instrumentation. http://kns.cnki.net/kcms/detail/23.1202.TH.20200930.1702.012.html (2020). Accessed 6 October 2020
- Zhang, J.W., Liu, Y., Zhang, D.P., Zhang, H.Y.: A new method of combined denoising based on CEEMDAN and wavelet adaptive thresholding. Electr. Meas. Instrum. 55(10), 14-18+33 (2018). https://doi.org/10.3969/j.issn.1001-1390.2018.10.003
- Xia, J.: Adaptive denoising method of power quality signal based on EEMD and multi-resolution SVD. Shandong Electric Power. 47(04), 45-50 (2020). https://doi.org/10.3969/j.issn.1007-9904.2020.04.010
- Cai, X.F., Zhang, H.B., Lu, G.F.: Improvement algorithm for harmonic analysis of power system using triple-spectrum-line interpolation algorithm based on window FFT. Power Syst. Prot. Control. 43(02), 33-39 (2015). https://doi.org/10.7667/j.issn.1674-3415.2015.02.006