SpringerLink
Log in

An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid

  • Original Paper
  • Published:
Electrical Engineering Aims and scope Submit manuscript

Abstract

DC microgrids are gaining more importance in maritime, aerospace, telecom, and isolated power plants for heightened reliability, efficiency, and control. Yet, designing a protective system for DC microgrids is challenging due to novelty and limited literature. Recent interest emphasizes standalone fault detection and classification, especially through data-driven machine-learning approaches. However, the emphasis remains on progressing state-of-the-art tools for fault diagnosis in DC microgrids. Therefore, this work emphasizes fault detection and classification in a low-voltage standalone DC microgrid using a data-driven machine learning hybrid approach: bagged ensemble learner and cosine k-nearest neighbour (C-kNN) algorithms. The proposed fault detection and classification scheme makes the use of local voltage and current measurements which enhances the admissibility of the proposed scheme. The bagged ensemble learner accurately identifies the faults in the line, whereas the cosine k-nearest neighbor classifies the fault as pole to ground or pole to pole for further corrective actions. A diverse set of test scenarios encompassing faulty and normal conditions has been analyzed and validated by randomizing data inputs. The test model comprising PV, battery source, and loads have been constructed in MATLAB/Simulink environment. The proposed scheme promises accurate fault identification and classification in normal and noisy environments. To establish the robustness of the proposed approach, the outcomes of the fault detection and classification scheme have been compared with the methods reported in the literature. The results indicate that the proposed method outperformed in comparison to existing methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Not Applicable.

Abbreviations

DC:

Direct current

EL:

Ensemble learner

LV:

Low voltage

MG:

Microgrid

PP:

Pole-to-pole

DG:

Distributed generation

ML:

Machine learning

PV:

Photovoltaic

BEL:

Bagged ensemble learner

C-kNN:

Cosine k-nearest neighbor

PG:

Pole-to-ground

2PG:

Two pole-to-ground

AI:

Artificial intelligence

HIF:

High-impedance faults

References

  1. Chowdhury D, Hasan AK, Khan MZR (2020) Islanded DC microgrid architecture with dual active bridge converter-based power management units and time slot-based control interface. IEEJ Trans Electr Electron Eng 15:863–871

    Article  Google Scholar 

  2. Hasan ASMK, Chowdhury D, Khan MZR (2018) Scalable DC microgrid architecture with a one-way communication-based control Interface. In: Proceedings 2018 10th international conference on electrical and computer engineering (ICECE), Dhaka, Bangladesh, 20–22; pp 265–268

  3. Chowdhury D, Hasan ASMK, Khan MZR (2018) Scalable DC microgrid architecture with phase-shifted full bridge converter-based power management unit. In: Proceedings 2018 10th international conference on electrical and computer engineering (ICECE), Dhaka, Bangladesh, 20–22; pp 22–25

  4. Mirsaeidi S, Dong X, Said DM (2018) Towards hybrid AC/DC microgrids: critical analysis and classification of protection strategies. Renew Sustain Energy Rev 90:97–103

    Article  Google Scholar 

  5. Beheshtaein S, Cuzner RM, Forouzesh M, Savagheb M, Guerrero JM (2019) DC microgrid protection: a comprehensive review. IEEE J Emerg Sel Top Power Electron 149:111401

    Google Scholar 

  6. Grcić I, Pandžić H, Novosel D (2021) Fault detection in DC microgrids using short-time fourier transform. Energies 14:277. https://doi.org/10.3390/en14020277

    Article  Google Scholar 

  7. Dagar A, Gupta P, Niranjan V (2021) Microgrid protection: a comprehensive review. Renew Sustain Energy Rev 149:111401. https://doi.org/10.1016/j.rser.2021.111401

    Article  Google Scholar 

  8. Li C, Rakhra P, Norman PJ, Burt GM, Clarkson P (2021) Multi-sample differential protection scheme in DC Microgrids. IEEE J Emerg Sel Top Power Electr 9(3):2560–2573. https://doi.org/10.1109/JESTPE.2020.3005588

    Article  Google Scholar 

  9. Prince SK, Affijulla S, Panda G (2023) Protection of DC microgrids based on complex power during faults in on/off-grid scenarios. IEEE Trans Ind Appl 59(1):244–254. https://doi.org/10.1109/TIA.2022.3206171

    Article  Google Scholar 

  10. Sistani A, Hosseini SA, Sadeghi VS, Taheri B (2023) Fault detection in a single-bus DC microgrid connected to EV/PV systems and hybrid energy storage using the DMD-IF method. Sustainability 15:16269. https://doi.org/10.3390/su152316269

    Article  Google Scholar 

  11. Haron AR, Mohamed A, Shareef H, Zayandehroodi H (2012) Analysis and solutions of overcurrent protection issues in a microgrid. In: Proceedings of the IEEE International Conference on Power and Energy (PECon), Kota Kinabalu, Malaysia; pp 644–649

  12. Farhadi M, Mohammed OA (2015) Event-based protection scheme for a multiterminal hybrid DC power system. IEEE Trans Smart Grid 6:1658–1669

    Article  Google Scholar 

  13. Yugeswar RO, Soumesh C, Chakraborty AK (2022) Bilayered fault detection and classification scheme for low-voltage DC microgrid with weighted KNN and decision tree. Int J Green Energy 19:11

    Google Scholar 

  14. Marija Č, Hrvoje P, Juraj H (2023) Mathematical morphology-based fault detection in radial DC microgrids considering fault current from VSC. IEEE Transactions on Smart Grid. https://doi.org/10.1109/TSG.2022.3229979,14,4,(2545-2557)

    Article  Google Scholar 

  15. Som S (2018) Efficient protection scheme for low-voltage DC micro-grid. IET Gener Transm Distrib 12:3322–3329

    Article  Google Scholar 

  16. Shen WC, Chen YH, Wu AY (2014) Low-complexity sinusoidal-assisted EMD (SAEMD) algorithms for solving mode-mixing problems in HHT. Digit Signal Process 24:170–186

    Article  Google Scholar 

  17. Sarangi S, Sahu BK, Rout PK (2020) A comprehensive review of distribution generation integrated DC microgrid protection: issues, strategies, and future direction. Int J Energy Res 45:5006–5031

    Article  Google Scholar 

  18. Verma A, Yadav A (2015) ANN-based fault detection and direction estimation scheme for series compensated transmission lines. In: Proceedings of the IEEE International Conference on electrical, computer and communication technologie (ICECCT), Coimbatore, India, 5–7, pp 1–6

  19. Li W, Monti A, Ponci F (2014) Fault detection and classification in medium voltage DC shipboard power systems with wavelets and artificial neural networks. IEEE Trans Instru Meas 63(11):2651–2665

    Article  Google Scholar 

  20. Yue M, Atif M, Damian O, Keith C (2022) Wavelet transform data-driven machine learning-based real-time fault detection for naval DC pulsating loads. IEEE Trans Transp Electr 8(2):1956–1965

    Article  Google Scholar 

  21. Manohar M., Koley E (2017) SVM based protection scheme for microgrid. In: international conference on intelligent computing, instrumentation and control technologies (ICICICT). Kerala, India: IEEE

  22. Delpha C, Diallo D, Samrout Al H, Moubayed N (2018) Multiple incipient fault diagnosis in three-phase electrical systems using multivariate statistical signal processing. Engg App Artif Intell 73:68–79

    Article  Google Scholar 

  23. Asadi M, Samet A, Ghanbari T (2017) k-NN based fault detection and classification methods for power transmission systems. Prot Control Mod Power Syst 2:32

    Article  Google Scholar 

  24. Manohar M, Pravat KR (2017) Detection and classification of micro-grid faults based on HHT and machine learning techniques. IET Gener Trans Distrib 12(2):388–397

    Google Scholar 

  25. Somesh L, Chakravarty A, Maiti AD (2022) Fault diagnosis in power transmission line using decision tree and random forest classifier. Durgapur, India, In: IEEE 6th international conference on condition assess. Techniques in electrical systems (CATCON)

  26. Amal Krishna TS, Hari Kumar R (2023) Fault detection and classification for DC microgrid using binary classification models. Thiruvananthapuram, India, In: international conference on control, Communication and Computing (ICCC)

  27. Ying-Yi H, Mark MCTA (2019) Fault detection, classification, and location by static switch in microgrids using wavelet transform and taguchi-based artificial neural network. IEEE Syst J 14(2):2725–2735

    Google Scholar 

  28. Abdali A, Mazlumi K, Noroozian R (2019) High-speed fault detection and location in DC microgrids systems using multi-criterion system and neural network. Appl Soft Comput 79:341–353

    Article  Google Scholar 

  29. Yang Q, Li J, Blond LS, Wang C (2016) Artificial neural network-based fault detection and fault location in the DC microgrid. Energy Proc 103:129–134

    Article  Google Scholar 

  30. Som S, Samantaray SR (2018) Efficient protection scheme for low-voltage DC micro-grid. IET Gener Transm Distrib 12(13):3322–3329

    Article  Google Scholar 

  31. Jayamaha DKJS, Lidula NWA, Rajapakse AD (2019) Wavelet multi resolution analysis-based ANN architecture for fault detection and localization in DC microgrids. IEEE Access 7:145371–145384

    Article  Google Scholar 

  32. Mohanty R, Sahoo S, Pradhan AK, Blaabjerg F (2021) A cosine similarity based centralized protection scheme for DC microgrids. IEEE J Emerg Sel Topics Power Electr 9(5):1–1

    Google Scholar 

  33. Fahim SR, Sarker S, Muyeen SMRI, Das SK (2020) Microgrid fault detection and classification: machine learning based approach, comparison, and reviews. Energies 13(13):3460

    Article  Google Scholar 

  34. Chen K, Hu J, He J (2016) Detection and classification of transmission line faults based on unsupervised feature learning and convolutional sparse autoencoder. IEEE Trans Smart Grid 9:1748–1758

    Google Scholar 

  35. Jayamaha DKJS, Lidula NWA, Rajapakse AD (2019) Wavelet based artificial neural networks for detection and classification of DC microgrid faults. IEEE power & energy society general meeting (PESGM)

  36. Chen CI, Lan CK, Chen YC, Chen CH, Chang CH (2020) Wavelet energy fuzzy neural network-based fault protection system for microgrid. Energies MDPI 13(4):1–13

    Google Scholar 

  37. Chatterjee S, Roy BKS (2020) Bagged tree based anti-islanding scheme for multidg microgrids. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02324-0

    Article  Google Scholar 

  38. Islam M, Usman M, Newaz A, Faruque M (2022) Ensemble voting-based fault classification and location identification for a distribution system with microgrids using smart meter measurements. IET Smart Grid 6(3):219–232. https://doi.org/10.1049/stg2.12091

    Article  Google Scholar 

  39. Wang T, Tan Y, Wang Y, ** B, Monti A, Sangiovanni-Vincentelli AL (2022) Synthetic data in DC microgrids: label creation for ensemble learning for fault isolation. IEEE Trans Power Deliv 37(3):2301–2313. https://doi.org/10.1109/TPWRD.2021.3110182

    Article  Google Scholar 

  40. Okfalisa, Gazalba I, Mustakim, Reza NGI (2017) Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification. In: proceedings of the 2nd international conferences on information technology, information systems and electrical engineering (ICITISEE), Yogyakarta, Indonesia, 1–3, 294–298

  41. Deb A, Jain AK (2023) Design of a robust and fast fault diagnosis strategy of a stand-alone LVDCMG using gaussian SVM & weighted KNN algorithms, In: 3rd international conference on energy, power and electrical engineering (EPEE), Wuhan, China, 2023, pp. 1247–1253, doi: https://doi.org/10.1109/EPEE59859.2023.10352007.

  42. Gangwar AK, Shaik AG (2023) k-Nearest neighbour-based approach for the protection of distribution network with renewable energy integration. Electr Power Syst Res 220:109301. https://doi.org/10.1016/j.epsr.2023.109301

    Article  Google Scholar 

  43. Grcić I, Pandžić H (2021) Fault detection in DC microgrids using recurrent neural networks. In: international conference on smart energy system and technologies (SEST), Vaasa, Finland, 1–6

  44. Saxena A, Sharma NK, Samantaray SR (2022) An enhanced differential protection scheme for LVDC microgrid. IEEE J Emerg Sel Top Power Electr 10(2):2114–2125

    Article  Google Scholar 

  45. Meskin M, Domijan A, Grinberg I (2020) Impact of distributed generation on the protection systems of distribution networks: analysis and remedies—review paper. IET Gener Transmiss Distrib 14(24):5944–5960

    Article  Google Scholar 

  46. Zhou Z (2021) Ensemble learning. Machine learning. Springer, Singapore, pp 181–210

    Book  Google Scholar 

  47. Breiman L (1996) Bagging predictors. Mach Learn 24:123–140

    Article  Google Scholar 

  48. Deleplace A, Atamuradov V, Allali A, Pellé J, Plana R, Alleaume G (2020) Ensemble learning-based fault detection in nuclear power plant screen cleaners. IFAC-Papers On-Line 53(2):10354–10359

    Article  Google Scholar 

  49. Gangavva C, Mangai J, Bansal M (2022) Advances in parallel computing algorithms, tools and paradigms: an investigation of ensemble learning algorithms for fault diagnosis of roller bearing, pp-117–125

  50. Naimi A, Deng J, Doney P, Sheikh-Akbari A, Shimjith SR, Arul AJ (2022) Machine learning-based fault diagnosis for a PWR nuclear power plant. IEEE Access 10:126001–126010

    Article  Google Scholar 

  51. Johnson JM, Yadav A (2016) Fault detection and classification technique for HVDC transmission lines using KNN. International Conference on ICT for sustainable development ICT4SD, LNNS, 10

  52. Majumdar A, Jain AK, Debnath R (2023) Assessment of transient disturbance using discrete fourier transform and feed forward neural network based hybrid classifier. In: 2023 IEEE 3rd international conference on sustainable energy and future electric transportation (SEFET), Bhubaneswar, India, pp. 1–6, doi: https://doi.org/10.1109/SeFeT57834.2023.10245639.

  53. Karan S, Yeh HG (2020) Fault classification in microgrids using deep learning, In: 2020 IEEE green energy and smart systems conference (IGESSC), Long Beach, CA, USA, pp 1–7, https://doi.org/10.1109/IGESSC50231.2020.9285101

  54. Nayak S, Bhat M, Reddy NVS, Rao BA (2022) Study of distance metrics on k - nearest neighbor algorithm for star categorization. J Phys Conf Ser 2161:012004

    Article  Google Scholar 

Download references

Funding

Not Applicable.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, NIT Agartala, Jirania, Tripura, 799046, India

    Anindita Deb

  2. Department of Electrical Engineering, Faculty of Engineering, NIT Agartala, Jirania, Tripura, 799046, India

    Arvind Kumar Jain

Authors
  1. Anindita Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arvind Kumar Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AD wrote the main manuscript text and prepared all the Figures. AKJ prepared the Tables & reviewed the manuscript.

Corresponding author

Correspondence to Anindita Deb.

Ethics declarations

Conflict of interest

There are no competing interests, according to the authors. The authors further state that none of their known conflicting financial interests or personal connections could exist to have an impact on the work presented in this study.

Ethical approval

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deb, A., Jain, A.K. An effective data-driven machine learning hybrid approach for fault detection and classification in a standalone low-voltage DC microgrid. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02334-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00202-024-02334-7

Keywords

Search

Navigation