Skip to main content
SpringerLink
Log in

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Power quality disturbances (PQDs) commonly occur in large-scale power systems and networks that rise critical issues. Therefore, an automated recognition and mitigation of PQDs is necessary. In this scenario, an efficient adaptive rate technique is proposed for viable features mining and recognition of PQDs. Event-driven A/D converters (EDADCs) are used to acquire PQD signals. To accurately segment the sampled signal, an appealing approach is used. A time-domain evaluation is carried out in the next phase to investigate the characteristics of these fragments. The mature machine learning algorithms are employed to carry out the classification. Compared to conventional counterparts, the findings indicate a decrease of 13.26 times in collected information. An average maximum identification precision of 99.33% is achieved by the proposed method. Compared to predecessors, this confirms the considerable performance of the processing and power usage of the engineered solution while achieving high recognition accuracy.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Van den Broeck G, Stuyts J, Driesen J. A critical review of power quality standards and definitions applied to DC microgrids. Appl Energy. 2018;229:281–8.

    Article  Google Scholar 

  2. Kumar V, Pandey AS, Sinha SK. Grid integration and power quality issues of wind and solar energy system: a review. Int Conf Emerg Trends Electr Electron Sustain Energy Syst (ICETEESES). 2016;2016:71–80.

    Article  Google Scholar 

  3. Rönnberg S, Bollen M. Power quality issues in the electric power system of the future. Electr J. 2016;29(10):49–61.

    Google Scholar 

  4. Ali ND, Zakri AA. Identifying characteristic of power quality problems on solar electric power generation. Int J Electr Energy Power Syst Eng. 2018;1(2):7–10.

    Google Scholar 

  5. Latran MB, Teke A, Yoldaş Y. Mitigation of power quality problems using distribution static synchronous compensator: a comprehensive review. IET Power Electron. 2015;8(7):1312–28.

    Article  Google Scholar 

  6. Thapar A, Saha TK, Dong ZY. Investigation of power quality categorisation and simulating it’s impact on sensitive electronic equipment. IEEE Power Eng Soc General Meet. 2004;1:528–33. https://doi.org/10.1109/PES.2004.1372855.

    Article  Google Scholar 

  7. R. A. Flores, “State of the art in the classification of power quality events, an overview,” in 10th International Conference on Harmonics and Quality of Power. Proceedings (Cat. No. 02EX630), 2002, vol. 1, pp. 17–20.

  8. Kow KW, Wong YW, Rajkumar RK, Rajkumar RK. A review on performance of artificial intelligence and conventional method in mitigating PV grid-tied related power quality events. Renew Sustain Energy Rev. 2016;56:334–46.

    Article  Google Scholar 

  9. Borges FA, Fernandes RA, Silva IN, Silva CB. Feature extraction and power quality disturbances classification using smart meters signals. IEEE Trans Ind Inform. 2015;12(2):824–33.

    Article  Google Scholar 

  10. Elphick S, Smith V, Gosbell V, Perera S, Ciufo P, Drury G. Characteristics of power quality disturbances in Australia: voltage dips at low-voltage sites. IET Gener Transm Distrib. 2015;9(15):2382–8.

    Article  Google Scholar 

  11. R. Igual, C. Medrano, F. J. Arcega, and G. Mantescu, “Integral mathematical model of power quality disturbances,” in 18th International Conference on Harmonics and Quality of Power (ICHQP) 2018, pp. 1–6.

  12. Mahela OP, Shaik AG, Gupta N. A critical review of detection and classification of power quality events. Renew Sustain Energy Rev. 2015;41:495–505.

    Article  Google Scholar 

  13. Khokhar S, Zin AABM, Mokhtar ASB, Pesaran M. A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renew Sustain Energy Rev. 2015;51:1650–63.

    Article  Google Scholar 

  14. Hossain E, Tür MR, Padmanaban S, Ay S, Khan I. Analysis and mitigation of power quality issues in distributed generation systems using custom power devices. IEEE Access. 2018;6:16816–33. https://doi.org/10.1109/ACCESS.2018.2814981.

    Article  Google Scholar 

  15. V. K. Ingle and J. G. Proakis, Digital signal processing using matlab: a problem solving companion. Cengage Learning, Boston, MA, 2016.

  16. Qaisar SM. Efficient mobile systems based on adaptive rate signal processing. Comput Electr Eng. 2019;79(106462):2019.

    Google Scholar 

  17. Mina Qaisar S, Sidiya D, Akbar M, Subasi A. An event-driven multiple objects surveillance system. Int J Electr Comput Eng Syst. 2018;9(1):35–44.

    Google Scholar 

  18. S. M. Qaisar, L. Fesquet, and M. Renaudin, “Effective resolution of an adaptive rate ADC,” in 8th International Conference on Sampling Theory and Applications (SAMPTA’09), Marseille, France, 2009.

  19. Qaisar SM, Subasi A. Cloud-based ECG monitoring using event-driven ECG acquisition and machine learning techniques. Phys Eng Sci Med. 2020;43(2):623–34.

    Article  Google Scholar 

  20. Mian Qaisar S. Signal-piloted processing and machine learning based efficient power quality disturbances recognition. PLoS ONE. 2021;16(5):e0252104.

    Article  Google Scholar 

  21. S. M. Qaisar and S. F. Hussain, “An effective arrhythmia classification via ECG signal subsampling and mutual information based subbands statistical features selection,” J. Ambient Intell. Humaniz. Comput. 2021. https://doi.org/10.1007/s12652-021-03275-w.

  22. H. Ian, E. Frank, M. Hall, and J. Christopher, “Data mining: Practical machine learning tools and techniques—Part II: More advanced machine learning schemes.” Morgan Kaufmann, Burlington, MA, 2017.

  23. Moser BA, Lunglmayr M. On quasi-isometry of threshold-based sampling. IEEE Trans Signal Process. 2019;67(14):3832–41.

    Article  MathSciNet  Google Scholar 

  24. R. Gandhi, “Support Vector Machine — Introduction to Machine Learning Algorithms,” Medium, Jul. 05, 2018. https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47 Accessed Apr 08, 2020.

  25. Suthaharan S. Machine learning models and algorithms for big data classification. Integr Ser Inf Syst. 2016;36:1–12.

    MathSciNet  MATH  Google Scholar 

  26. A. Sabo and S. M. Qaisar, “The event-driven power efficient wireless sensor nodes for monitoring of insects and health of plants,” in 3rd International Conference on Signal and Image Processing (ICSIP), 2018, pp. 478–483.

  27. Mian Qaisar S. Event-driven coulomb counting for effective online approximation of Li-ion battery state of charge. Energies. 2020;13(21):5600.

    Article  Google Scholar 

  28. Qaisar SM. Baseline wander and power-line interference elimination of ECG signals using efficient signal-piloted filtering. Healthc Technol Lett. 2020;7(4):114–8.

    Article  Google Scholar 

Download references

Funding

This project is funded by Effat University, Jeddah, KSA under the Grant number UC#9/29 April.2020/7.1-22(2)3.

Author information

Authors and Affiliations

  1. College of Engineering, Effat University, Jeddah, 22332, Saudi Arabia

    Saeed Mian Qaisar & Nehal Alyamani

  2. Communication and Signal Processing Lab, Energy and Technology Center, Effat University, Jeddah, 22332, Saudi Arabia

    Saeed Mian Qaisar

  3. Department of Electrical Engineering, Bahria University, Islamabad, 44000, Pakistan

    Asad Waqar

  4. Faculty of CSIT, Al-Baha University, Al-Baha, 65731, Saudi Arabia

    Moez Krichen

Authors
  1. Saeed Mian Qaisar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nehal Alyamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asad Waqar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Moez Krichen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saeed Mian Qaisar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

This article is part of the topical collection “Intelligent Systems” guest edited by Geetha Ganesan, Lalit Garg, Renu Dhir, Vijay Kumar and Manik Sharma.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mian Qaisar, S., Alyamani, N., Waqar, A. et al. Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification. SN COMPUT. SCI. 3, 14 (2022). https://doi.org/10.1007/s42979-021-00904-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-021-00904-1

Keywords

Search

Navigation