A Comparative Study of Signal Analysis Methods Applied in the Detection of Instantaneous Frequency
The smart grid concept is being applied more and more frequently and this is due to the need to integrate all the components that are part of power systems today, starting from generation units, storage systems, communications and connected loads. Non-linear and non-stationary signals have been obtained in this type of systems, which have high penetration of non-conventional energy sources (NCSRE) and non-linear loads. The power quality criterion has had to be adapted to the new conditions of the electrical systems and this has led to the need to search for new analysis methodologies for the acquired signals. In this article we present a review on non-linear and non-stationary signal analysis methods in electrical systems with high NCSRE penetration. To this end we explore the application of the Hilbert-Huang Transform (HHT), Wavelet Transform (WT) and Wigner-Ville Distribution (WVD), exposing each of the advantages and disadvantages of these methods. To validate the methodology, we have selected some synthetic signals that adequately describe the typical behaviors in these systems.
(2009). IEEE recommended practice for monitoring electric power quality.IEEE Std 1159-2009 (Revision of IEEE Std1159-1995), pages c1–81. doi:10.1109/IEEESTD.2009.5154067.
Afroni, M. J., Sutanto, D., and Stirling, D. (2013). Analysis of nonstationary power-quality waveforms using iterativehilbert huang transform and sax algorithm.IEEE Transactions on Power Delivery, 28(4):2134–2144.
Alshahrani, S., Abbod, M., and Taylor, G. (2016). Detection and classification of power quality disturbances based onhilbert-huang transform and feed forward neural networks. In 2016 51st International Universities Power EngineeringConference (UPEC), pages 1–6. IEEE.
Anzalchi, A., Sundararajan, A., Moghadasi, A., and Sarwat, A. (2019). High-penetration grid-tied photovoltaics:Analysis of power quality and feeder voltage profile. IEEE Industry Applications Magazine, 25(5):83–94.doi:10.1109/MIAS.2019.2923104.
Bíscaro, A., Pereira, R., Kezunovic, M., and Mantovani, J. (2015). Integrated fault location and power-qualityanalysis in electric power distribution systems.IEEE Transactions on power delivery, 31(2):428–436.
Bueno-Lopez, M., Molinas, M., and Kulia, G. (2017).Understanding instantaneous frequency detection: Adiscussion of Hilbert-Huang Transform versus Wavelet Transform. In International Work-Conference on Time SeriesAnalysis-ITISE, volume 1, pages 474–486, Granada, Spain. University of Granada.
Deering, R. and Kaiser, J. F. (2005). The use of a masking signal to improve empirical mode decomposition. InProceedings.(ICASSP’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., volume 4,pages iv–485. IEEE.
Drummond, C. F. and Sutanto, D. (2010). Classification of power quality disturbances using the iterative hilberthuang transform. In Proceedings of 14th International Conference on Harmonics and Quality of Power - ICHQP 2010,pages 1–7. doi:10.1109/ICHQP.2010.5625326.
Fan, Z. and Liu, X. (2012). A novel universal grid voltage sag detection algorithm. In2012 Power Engineering andAutomation Conference, pages 1–4. IEEE.
Gasca, M. V., Bueno-Lopez, M., Molinas, M., and Fosso, O. B. (2018). Time-frequency analysis for nonlinearand non-stationary signals using hht: A mode mixing separation technique. IEEE Latin America Transactions,16(4):1091–1098. doi:10.1109/TLA.2018.8362142.
Golpîra, H. and Messina, A. R. (2018). A center-of-gravity-based approach to estimate slow power and frequencyvariations.IEEE Transactions on Power Systems, 33(1):1026–1035. doi:10.1109/TPWRS.2017.2710187. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H. (1998).The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis.Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971):903–995.
Kavitha, V. and Subramanian, K. (2017). Investigation of power quality issues and its solution for distributedpower system. In 2017 International Conference on Circuit ,Power and Computing Technologies (ICCPCT), pages 1–6.doi:10.1109/ICCPCT.2017.8074372.
Kornatka, M. (2017). Distribution of saidi and saifi indices and the saturation of the mv network with remotelycontrolled switches. In 2017 18th International Scientific Conference on Electric Power Engineering (EPE), pages 1–4.doi:10.1109/EPE.2017.7967243.
Kumar, D. and Zare, F. (2016).Harmonic analysis of grid connected power electronic systems in lowvoltage distribution networks.IEEE Journal of Emerging and Selected Topics in Power Electronics, 4(1):70–79.doi:10.1109/JESTPE.2015.2454537.
Leonowicz, Z. (2000). Analysis of non-stationary signals in power systems using wigner transform and min-normmethod. In 7th EEEIC Conference on Environment and Electrical Engineering, pages 43–46.Liu, Z., Cui, Y., and Li, W. (2015). A classification method for complex power quality disturbances using eemd andrank wavelet svm.IEEE Transactions on Smart Grid, 6(4):1678–1685. doi:10.1109/TSG.2015.2397431.
Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation.IEEE Transactionson Pattern Analysis & Machine Intelligence, (7):674–693.
Naderi, Y., Hosseini, S. H., Ghassemzadeh, S., Mohammadi-Ivatloo, B., Savaghebi, M., Vasquez, J. C., and Guerrero,J. M. (2020). Chapter 4 - Power quality issues of smart microgrids: applied techniques and decision making analysis. In Aleem, S. H. A., Abdelaziz, A. Y., Zobaa, A. F., and Bansal, R., editors,Decision Making Applications in ModernPower Systems, pages 89 – 119. Academic Press. doi:https://doi.org/10.1016/B978-0-12-816445-7.00004-9.
O’Toole, J. M., Mesbah, M., and Boashash, B. (2008). A new discrete analytic signal for reducing aliasing in thediscrete wigner-ville distribution.IEEE Transactions on Signal Processing, 56(11):5427–5434.
Puliafito, V., Vergura, S., and Carpentieri, M. (2017). Fourier, wavelet, and hilbert-huang transforms for studyingelectrical users in the time and frequency domain.Energies, 10(2). doi:10.3390/en10020188.
Sahani, M. and Dash, P. (2019). Fpga-based online power quality disturbances monitoring using reduced-samplehht and class-specific weighted rvfln.IEEE Transactions on Industrial Informatics.Sahani, M. and Dash, P. K. (2018). Automatic power quality events recognition based on hilbert huang transformand weighted bidirectional extreme learning machine.IEEE Transactions on Industrial Informatics, 14(9):3849–3858.
Sanabria-Villamizar, M., Bueno-López, M., Molinas, M., and Bernal, E. (2019). Hybrid technique for the analysis ofnon-linear and non-stationary signals focused on power quality. In2019 FISE-IEEE/CIGRE Conference-Living theenergy Transition (FISE/CIGRE), pages 1–6. IEEE.
Senroy, N., Suryanarayanan, S., and Ribeiro, P. F. (2007).An improved hilbert–huang method foranalysis of time-varying waveforms in power quality.IEEE Transactions on Power Systems, 22(4):1843–1850.doi:10.1109/TPWRS.2007.907542.