Skip to content
2000
Volume 19, Issue 8
  • ISSN: 1872-2121
  • E-ISSN: 2212-4047

Abstract

Introduction/Background

The development of smart grids requires energy meters that enable accurate measurements.

Objective

In response to the current issue where Direct Current (DC) energy meters only measure DC components and not ripple components, we propose to design a novel algorithm to achieve accurate separation of DC components and ripple components in DC signals.

Methodology

This patent proposes a precise detection algorithm for ripple components. The algorithm is based on the Wavelet Decomposition combined with Flamingo Search Algorithm-Variational Mode Decomposition (WD-FSA-VMD). Firstly, we analyze the ripple characteristics in the DC signal and use the Wavelet Decomposition (WD) algorithm to separate the ripple components in the DC signal accurately. Secondly, we utilize the Flamingo Search Algorithm (FSA) to optimize the number of modal decompositions and the penalty factor in the Variational Mode Decomposition (VMD). This allows us to accurately extract the ripple components. Finally, we conducted simulation experiments comparing the improved algorithm with the original algorithm.

Results and Discussion

The experimental results indicate that the signal correlation coefficient obtained by the WD- FSA-VMD algorithm increases by 82.64% compared to traditional VMD, and it increases by 16.72% compared to the combined Wavelet Decomposition with Whale Optimization Algorithm-Variational Mode Decomposition (WD-WOA-VMD).

Conclusion

The improved algorithm we proposed has good adaptability and separation performance. It is prospective for the new algorithm to provide a valuable reference to ripple detection technology.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/eng/10.2174/0118722121315053240904073956
2024-10-14
2025-12-24

  • From This Site

    /content/journals/eng/10.2174/0118722121315053240904073956
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. GuerreroJ.M. LohP.C. LeeT.L. ChandorkarM. Advanced control architectures for intelligent microgrids—Part II: Power quality, energy storage, and AC/DC microgrids.IEEE Trans. Ind. Electron.20136041263127010.1109/TIE.2012.2196889
     [Google Scholar]
  2. GräfKevin FischerauerGerhard Qualification of direct current electricity meters for fast charging stations.tm - Technical Measuring2021988499507
     [Google Scholar]
  3. WangZ.C. PengY.F. LiuY.F. HeK.F. WuZ.T. LiB.Q. The calculation method of PV direct current energy based on modulated broadband mode decomposition and compound simpson integral algorithm.IEEE Access20219514035141510.1109/ACCESS.2021.3067728
     [Google Scholar]
  4. NazirH.M. HussainI. FaisalM. ElashkarE.E. ShoukryA.M. Improving the prediction accuracy of river inflow using two data pre-processing techniques coupled with data-driven model.PeerJ20197e804310.7717/peerj.804331871832
     [Google Scholar]
  5. AfroniM.J. SutantoD. StirlingD. Analysis of nonstationary power-quality waveforms using iterative hilbert huang transform and SAX algorithm.IEEE Trans. Power Deliv.20132842134214410.1109/TPWRD.2013.2264948
     [Google Scholar]
  6. XianK. Research on DC Metering Error Analysis and Improved Algorithm for Electric Vehicles Considering Ripple Effects.Shandong University of Science and Technology2021
     [Google Scholar]
  7. LiuJ. XuQ. TianZ. YuxuanG. ShunranQ. LihuiW. Feature extraction method for DC charging signal of electric vehicle.J. Phys.: Conf. Ser.201810691012114IOP Publishing
     [Google Scholar]
  8. HaoW. Research on the measurement error analysis and measurement accuracy improvement method of DC charging pile DC meter.Harbin University of Science and Technology2021
     [Google Scholar]
  9. ThangarajK. MuruganandhamJ. SelvaumarS. JaganR. Analysis of harmonics using S-Transform.2016 International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), Pudukkottai, India. 2016, 1-5.
     [Google Scholar]
  10. Du XudongLuo Derong TingWu DC microgrid ripple detection method based on improved VMD algorithm.Electr. Meas. Instrum.202360103642
     [Google Scholar]
  11. SharmaA. LiC. ShenH. WangH. WangY. LiB. LiC. GuoR. Nonlinear harmonic electric energy metering system based on the wavelet transform.Recent Adv. Electr. Electron. Eng.202316211011910.2174/2352096515666220428154650
     [Google Scholar]
  12. AslayF. TingN.S. Machine learning-based estimation of output current ripple in PFC-IBC used in battery charger of electrical vehicles: A comparison of LR, RF and ANN techniques.IEEE Access202210500785008610.1109/ACCESS.2022.3174100
     [Google Scholar]
  13. JainV.K. CollinsW.L. DavisD.C. High-accuracy analog measurements via interpolated FFT.IEEE Trans. Instrum. Meas.197928211312210.1109/TIM.1979.4314779
     [Google Scholar]
  14. PhamD-H. MeignenS. A novel thresholding technique for the denoising of multicomponent signals.2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, Canada, 2018, pp. 4004-4008.10.1109/ICASSP.2018.8462216
     [Google Scholar]
  15. ZhangJ. ShenJ. JianY. Research on wavelet threshold denoising algorithm for dc charging pile energy metering data.Electrical Measurement and Instrumentation.20225906150157
     [Google Scholar]
  16. LiangX. ZhangH. LyuT. XuL. CaoC. GulliverT.A. Ultra-wide band impulse radar for life detection using wavelet packet decomposition.Phys. Commun.201829314710.1016/j.phycom.2018.04.004
     [Google Scholar]
  17. JiangW. MaD. WeiZ. FengZ. ZhangP. PengJ. ISAC-NET: Model-driven deep learning for integrated passive sensing and communication.IEEE Trans. Commun.20247284692470710.1109/TCOMM.2024.3375818
     [Google Scholar]
  18. BazziA. ChafiiM. On outage-based beamforming design for dual-functional radar-communication 6G systems.IEEE Trans. Wirel. Commun.20232285598561210.1109/TWC.2023.3235617
     [Google Scholar]
  19. BazziA. ChafiiM. Secure full duplex integrated sensing and communications.IEEE Trans. Inf. Forensics Security2024192082209710.1109/TIFS.2023.3346696
     [Google Scholar]
  20. BazziA. ChafiiM. On integrated sensing and communication waveforms with tunable PAPR.IEEE Trans. Wirel. Commun.202322117345736010.1109/TWC.2023.3250263
     [Google Scholar]
  21. MishraM. RoutP.K. Detection and classification of micro‐grid faults based on HHT and machine learning techniques.IET Gener. Transm. Distrib.201812238839710.1049/iet‑gtd.2017.0502
     [Google Scholar]
  22. DragomiretskiyK. ZossoD. Variational mode decomposition.IEEE Trans. Signal Process.201462353154410.1109/TSP.2013.2288675
     [Google Scholar]
  23. KhuramN. MuhammadT.A. MuhammadF.S. A statistical approach to signal denoising based on data-driven multiscale representation.Digit. Signal Process.2021108
     [Google Scholar]
  24. HadiyosoS. WijayantoI. DewiE.M. ECG based biometric identification system using EEMD.VMD and Renyi Entropy. International Conference on Information and Communication Technology (ICoICT), Yogyakarta, Indonesia. 2020, 8, pp. 1-5.
     [Google Scholar]
  25. WuZ. ZhangZ. ZhengL. YanT. TangC. The denoising method for transformer partial discharge based on the whale vmd algorithm combined with adaptive filtering and wavelet thresholding.Sensors20232319808510.3390/s2319808537836915
     [Google Scholar]
  26. G VT. Non-stationary thermal wave mode decomposition: A comparative study of EMD, HVD, and VMD for defect detection.Russ. J. Nondestr. Test.2022586
     [Google Scholar]
  27. ZhihengW. JianhuaL. Flamingo Search Algorithm: A New Swarm Intelligence Optimization Algorithm.IEEE Access20219885648858210.1109/ACCESS.2021.3090512
     [Google Scholar]
  28. QiS. Research on the Impact of Electric Vehicle Charging Load on the Accuracy of Electric Energy Metering.Southeast University2020
     [Google Scholar]
  29. MirjaliliS. LewisA. The whale optimization algorithm.Adv. Eng. Softw.201695
     [Google Scholar]
/content/journals/eng/10.2174/0118722121315053240904073956
Loading
Direct Current Ripple Component Detection Algorithm Based on Improved Variational Mode Decomposition
Recent Pat Eng 19, E18722121315053 (2025); https://doi.org/10.2174/0118722121315053240904073956
/content/journals/eng/10.2174/0118722121315053240904073956
/content/journals/eng/10.2174/0118722121315053240904073956
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Direct current energy; flamingo search algorithm; intelligence optimization algorithms; ripple extraction; transient ripple; variational mode decomposition; wavelet decomposition

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test