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Islanding detection in DC ring microgrid using improved complete ensemble empirical mode decomposition with adaptive noise and multi-class AdaBoost algorithm

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Abstract

To meet the energy demand and increase the reliability, DC microgrids are mainly preferred in the present era. Particularly, photovoltaic-based DC microgrids are considered due to ease of use, availability and economic operation. These DC microgrids face protection issues mainly due to absence of several parameters such as reactive power and frequency. Because of this many algorithms applied in ac networks are not valid. This study introduces a novel protection mechanism of proposed DC ring microgrid for islanding and non-islanding disturbance detection. The extracted DC signals are processed with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) for accurate detection. An ICEEMDAN method gives the intrinsic mode functions (IMFs) from which the best IMF is chosen by sparse kurtosis (SKI). The best IMF is passed through different indices like energy, mean, variance, etc., for retrieving the data. The acquired data are input to the multi-class AdaBoost approach for classification, which recognizes faults by modifying the distribution of data and iteratively adjusting the weight of each instance. The proposed system efficacy is tested using the MATLAB/Simulink platform under various operating situations such as load variation, irradiation and fault resistance changes. The proposed algorithm superiority is demonstrated by comparing it to existing approaches using confusion matrix (CM) parameters.

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References

  1. Mohanty R, Pradhan AK (2018) DC ring bus microgrid protection using the oscillation frequency and transient power. IEEE Syst J 13(1):875–884

    Article  ADS  Google Scholar 

  2. Sheikh AA, Wakode SA, Deshmukh RR and Ballal MS (2019) A protection scheme for fault detection, location and isolation in DC ring microgrid. In IECON 2019–45th annual conference of the IEEE industrial electronics society, vol 1. IEEE, pp 2109–2114

  3. Papadimitriou CN, Kleftakis VA, Hatziargyriou ND (2016) A novel method for islanding detection in DC networks. IEEE Trans Sustain Energy 8(1):441–448

    Article  ADS  Google Scholar 

  4. Choudhury BK, Jena P (2022) Superimposed impedance-based passive islanding detection scheme for DC microgrids. IEEE J Emerg Sel Top Power Electron 10(1):469–483. https://doi.org/10.1109/JESTPE.2021.3076459

    Article  Google Scholar 

  5. Makkieh A, Florida-James A, Tzelepis D, Emhemed A, Burt G, Strachan S and Junyent-Ferre A (2019) Assessment of passive islanding detection methods for DC In: 15th IET international conference on AC and DC power transmission (ACDC 2019), Coventry, UK, 5–7 Feb 2019, pp 1–6. ISBN 978-1-83953-007-4, https://doi.org/10.1049/cp.2019.0016

  6. Monadi M, Zamani MA, Candela JI, Luna A, Rodriguez P (2015) Protection of AC and DC distribution systems Embedding distributed energy resources: a comparative review and analysis. Renew Sustain Energy Rev 51:1578–1593

    Article  Google Scholar 

  7. Manohar M, Koley E, Ghosh S (2019) Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder. IET Renew Power Gener 13(2):308–317

    Article  Google Scholar 

  8. Dhar S, Patnaik RK, Dash PK (2017) Fault detection and location of photovoltaic based DC microgrid using differential protection strategy. IEEE Trans Smart Grid 9(5):4303–4312

    Article  Google Scholar 

  9. Yan X, Liu Y, Zhang W, Jia M, Wang X (2020) Research on a novel improved adaptive variational mode decomposition method in rotor fault diagnosis. Appl Sci 10(5):1696

    Article  CAS  Google Scholar 

  10. Das S, Pradhan AK, Kedia A, Dalai S, Chatterjee B, Chakravorti S (2018) Diagnosis of power quality events based on detrended fluctuation analysis. IEEE Trans Industr Electron 65(9):7322–7331

    Article  Google Scholar 

  11. Jayamaha DKJS, Lidula NWA, Rajapakse AD (2020) Wavelet based artificial neural networks for detection and classification of DC microgrid faults. In: 2019 IEEE power & energy society general meeting (PESGM), Atlanta, GA, USA, 4–8 Aug 2019, pp 1–5. https://doi.org/10.1109/PESGM40551.2019.8974108

  12. Naik J, Dhar S, Dash PK (2020) Effective fault diagnosis and distance calculation for photovoltaic-based DC microgrid using adaptive EWT and kernel random vector functional link network. IET Gener Transm Distrib 14(4):690–703

    Article  Google Scholar 

  13. Oh YS, Kim CH, Gwon GH, Noh CH, Bukhari SBA, Haider R, Gush T (2019) Fault detection scheme based on mathematical morphology in last mile radial low voltage DC distribution networks. Int J Electr Power Energy Syst 106:520–527

    Article  Google Scholar 

  14. Luo Y, Chen C, Kang S, Zhang P (2019) Fault diagnosis of rolling element bearing using an adaptive multiscale enhanced combination gradient morphological filter. Shock Vib. https://doi.org/10.1155/2019/2059631

    Article  Google Scholar 

  15. Wu XT, Yang M, Yuan XH, Gong TK (2015) Bearing fault diagnosis using EEMD and improved morphological filtering method based on kurtosis criterion. J Vib shock 34(2):38–44

    Google Scholar 

  16. Wang X, Song G, Gao J, Wei X, Wei Y, Mostafa K, Hu Z, Zhang Z (2019) High impedance fault detection method based on improved complete ensemble empirical mode decomposition for DC distribution network. Int J Electr Power Energy Syst 107:538–556

    Article  Google Scholar 

  17. Han H, Cho S, Kwon S, Cho SB (2018) Fault diagnosis using improved complete ensemble empirical mode decomposition with adaptive noise and power-based intrinsic mode function selection algorithm. Electronics 7(2):16

    Article  Google Scholar 

  18. Ke J, Zhengxuan Z, Zhe Y, Yu F, Tianshu B, Jiankang Z (2020) Intelligent islanding detection method for photovoltaic power system based on Adaboost algorithm. IET Gener Transm Distrib 14(18):3630–3640

    Article  Google Scholar 

  19. Xia T, Zhuo P, Xiao L, Du S, Wang D, Xi L (2021) Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm. Neurocomputing 433:237–251

    Article  Google Scholar 

  20. Baloch S, Muhammad MS (2021) An intelligent data mining-based fault detection and classification strategy for microgrid. IEEE Access 9:22470–22479

    Article  Google Scholar 

  21. Long Z, Zhang X, Zhang L, Qin G, Huang S, Song D, Shao H, Wu G (2021) Motor fault diagnosis using attention mechanism and improved adaboost driven by multi-sensor information. Measurement 170:108718

    Article  Google Scholar 

  22. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  MathSciNet  Google Scholar 

  23. Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Stat Interface 2(3):349–360

    Article  MathSciNet  Google Scholar 

  24. Hu J, Shan Y, Xu Y, Guerrero JM (2019) A coordinated control of hybrid ac/dc microgrids with PV-wind-battery under variable generation and load conditions. Int J Electr Power Energy Syst 104:583–592

    Article  Google Scholar 

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  1. Siksha O Anusandhan Deemed to Be University, Bhubaneswar, India

    Subinay Vajpayee & P. K. Dash

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  1. Subinay Vajpayee
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  2. P. K. Dash
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SV has contributed to detailed simulations. PKD has conceptualized the problem and edited the text.

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Correspondence to P. K. Dash.

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Vajpayee, S., Dash, P.K. Islanding detection in DC ring microgrid using improved complete ensemble empirical mode decomposition with adaptive noise and multi-class AdaBoost algorithm. Electr Eng 106, 369–383 (2024). https://doi.org/10.1007/s00202-023-01971-8

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