IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i24p8579-d706485.html
   My bibliography  Save this article

Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition

Author

Listed:
  • Linao Li

    (Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China)

  • Xinlao Wei

    (Key Laboratory of Engineering Dielectrics and Its Application, Ministry of Education, School of Electrical and Electronics Engineering, Harbin University of Science and Technology, Harbin 150080, China)

Abstract

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x ( t ) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x ( t ). IEMD is then performed on x ( t ). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3 σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

Suggested Citation

  • Linao Li & Xinlao Wei, 2021. "Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition," Energies, MDPI, vol. 14(24), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8579-:d:706485
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/24/8579/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/24/8579/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Linao Li & Xinlao Wei, 2022. "Power Interference Suppression Method for Measuring Partial Discharges under Pulse Square Voltage Conditions," Energies, MDPI, vol. 15(9), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2021. "Sustainable Supply Chains with Blockchain, IoT and RFID: A Simulation on Order Management," Sustainability, MDPI, vol. 13(11), pages 1-23, June.
    2. Valeria Costantini & Francesco Crespi & Giovanni Marin & Elena Paglialunga, 2016. "Eco-innovation, sustainable supply chains and environmental performance in European industries," LEM Papers Series 2016/19, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    3. Lee, Alice J. & Ames, Daniel R., 2017. "“I can’t pay more” versus “It’s not worth more”: Divergent effects of constraint and disparagement rationales in negotiations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 16-28.
    4. Hussain, Hadia & Murtaza, Murtaza & Ajmal, Areeb & Ahmed, Afreen & Khan, Muhammad Ovais Khalid, 2020. "A study on the effects of social media advertisement on consumer’s attitude and customer response," MPRA Paper 104675, University Library of Munich, Germany.
    5. A. G. Fatullayev & Nizami A. Gasilov & Şahin Emrah Amrahov, 2019. "Numerical solution of linear inhomogeneous fuzzy delay differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 315-326, September.
    6. Cyril Chalendard, 2015. "Use of internal information, external information acquisition and customs underreporting," Working Papers halshs-01179445, HAL.
    7. Arun Advani & William Elming & Jonathan Shaw, 2023. "The Dynamic Effects of Tax Audits," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 545-561, May.
    8. Philippe Aghion & Ufuk Akcigit & Matthieu Lequien & Stefanie Stantcheva, 2017. "Tax simplicity and heterogeneous learning," CEP Discussion Papers dp1516, Centre for Economic Performance, LSE.
    9. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    10. Chuangen Gao & Shuyang Gu & Jiguo Yu & Hai Du & Weili Wu, 2022. "Adaptive seeding for profit maximization in social networks," Journal of Global Optimization, Springer, vol. 82(2), pages 413-432, February.
    11. Koessler, Frederic & Laclau, Marie & Renault, Jérôme & Tomala, Tristan, 2022. "Long information design," Theoretical Economics, Econometric Society, vol. 17(2), May.
    12. Jamal El-Den & Pratap Adikhari & Pratap Adikhari, 2017. "Social media in the service of social entrepreneurship: Identifying factors for better services," Journal of Advances in Humanities and Social Sciences, Dr. Yi-Hsing Hsieh, vol. 3(2), pages 105-114.
    13. Annette Alstadsæter & Wojciech Kopczuk & Kjetil Telle, 2019. "Social networks and tax avoidance: evidence from a well-defined Norwegian tax shelter," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(6), pages 1291-1328, December.
    14. Xiongnan Jin & Sejin Chun & Jooik Jung & Kyong-Ho Lee, 0. "A fast and scalable approach for IoT service selection based on a physical service model," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    15. Jun Hong Park & Sang Ho Kook & Hyeonu Im & Soomin Eum & Chulung Lee, 2018. "Fabless Semiconductor Firms’ Financial Performance Determinant Factors: Product Platform Efficiency and Technological Capability," Sustainability, MDPI, vol. 10(10), pages 1-22, September.
    16. Sebastian Kaumanns, 2019. "“Some fuzzy math”: relational information on debt value adjustments by managers and the financial press," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 755-794, December.
    17. Samuel J Gershman, 2015. "A Unifying Probabilistic View of Associative Learning," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-20, November.
    18. Arun Advani, 2022. "Who does and doesn't pay taxes?," Fiscal Studies, John Wiley & Sons, vol. 43(1), pages 5-22, March.
    19. Steve Fortin & Ahmad Hammami & Michel Magnan, 2021. "Re‐exploring Fair Value Accounting and Value Relevance: An Examination of Underlying Securities," Abacus, Accounting Foundation, University of Sydney, vol. 57(2), pages 220-250, June.
    20. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8579-:d:706485. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.