IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i24p6459-d1549742.html
   My bibliography  Save this article

A Sparse Representation-Based Reconstruction Method of Electrical Impedance Imaging for Grounding Grid

Author

Listed:
  • Ke Zhu

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Donghui Luo

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Zhengzheng Fu

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Zhihang Xue

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

  • Xianghang Bu

    (State Grid Sichuan Electric Power Research Institute, Chengdu 610041, China)

Abstract

As a non-invasive imaging method, electrical impedance tomography (EIT) technology has become a research focus for grounding grid corrosion diagnosis. However, the existing algorithms have not produced ideal image reconstruction results. This article proposes an electrical impedance imaging method based on sparse representation, which can improve the accuracy of reconstructed images obviously. First, the basic principles of EIT are outlined, and the limitations of existing reconstruction methods are analyzed. Then, an EIT reconstruction algorithm based on sparse representation is proposed to address these limitations. It constructs constraints using the sparsity of conductivity distribution under a certain sparse basis and utilizes the accelerated Fast Iterative Shrinkage Threshold Algorithm (FISTA) for iterative solutions, aiming to improve the imaging quality and reconstruction accuracy. Finally, the grounding grid model is established by COMSOL simulation software to obtain voltage data, and the reconstruction effects of the Tikhonov regularization algorithm, the total variation regularization algorithm (TV), the one-step Newton algorithm (NOSER), and the sparse reconstruction algorithm proposed in this article are compared in MATLAB. The voltage relative error is introduced to evaluate the reconstructed image. The results show that the reconstruction algorithm based on sparse representation is superior to other methods in terms of reconstruction error and image quality. The relative error of the grounding grid reconstructed image is reduced by an average of 12.54%.

Suggested Citation

  • Ke Zhu & Donghui Luo & Zhengzheng Fu & Zhihang Xue & Xianghang Bu, 2024. "A Sparse Representation-Based Reconstruction Method of Electrical Impedance Imaging for Grounding Grid," Energies, MDPI, vol. 17(24), pages 1-16, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:24:p:6459-:d:1549742
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/24/6459/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/24/6459/
    Download Restriction: no
    ---><---

    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:17:y:2024:i:24:p:6459-:d:1549742. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.