IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i24p7917-d1294091.html
   My bibliography  Save this article

A High-Precision Error Calibration Technique for Current Transformers under the Influence of DC Bias

Author

Listed:
  • Sanlei Dang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China
    Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Yong Xiao

    (Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China)

  • Baoshuai Wang

    (Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China)

  • Dingqu Zhang

    (Metrology Center, Guangdong Power Grid Co., Ltd., Guangzhou 510080, China)

  • Bo Zhang

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China)

  • Shanshan Hu

    (Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China)

  • Hongtian Song

    (Electric Power Research Institute, China Southern Power Grid Company Limited, Guangzhou 510663, China
    Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou 510663, China)

  • Chi Xu

    (School of Electric Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

  • Yiqin Cai

    (School of Electric Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

A bias current in the power system will cause saturation of the measuring current transformer (CT), leading to an increase in measurement error. Therefore, in this paper, we first conducted measurements of the direct current component in a 10 kV distribution system. Subsequently, a reverse extraction method for the CT distorted current under direct current bias conditions based on Random Forest Classification (RFC) and Long Short-Term Memory (LSTM) was proposed. This method involves two stages for the reverse extraction of CT distorted currents under direct current bias conditions. In the offline stage, data samples were generated by changing the operating environment of the CT. The RFC classification algorithm was used to divide the saturation levels of the CT, and for each sub-class, Particle Swarm Optimization–Long Short-Term Memory Network (PSO-LSTM) models were trained to establish the mapping relationship between the secondary distorted current and the primary current fundamental component. In the online stage, the saturated data segments were extracted from the secondary current waveform using wavelet transform, and these segments were input into the offline model for current reverse extraction. The simulation results show that the proposed method exhibited strong robustness under various CT conditions, and achieved high reconstruction accuracy for the primary current.

Suggested Citation

  • Sanlei Dang & Yong Xiao & Baoshuai Wang & Dingqu Zhang & Bo Zhang & Shanshan Hu & Hongtian Song & Chi Xu & Yiqin Cai, 2023. "A High-Precision Error Calibration Technique for Current Transformers under the Influence of DC Bias," Energies, MDPI, vol. 16(24), pages 1-19, December.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:24:p:7917-:d:1294091
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/24/7917/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/24/7917/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Ali & Dae-Hee Son & Sang-Hee Kang & Soon-Ryul Nam, 2017. "An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy," Energies, MDPI, vol. 10(11), pages 1-24, November.
    2. Sopheap Key & Chang-Sung Ko & Kwang-Jae Song & Soon-Ryul Nam, 2023. "Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders," Energies, MDPI, vol. 16(3), pages 1-16, February.
    Full references (including those not matched with items on IDEAS)

    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. Ismoil Odinaev & Andrey Pazderin & Murodbek Safaraliev & Firuz Kamalov & Mihail Senyuk & Pavel Y. Gubin, 2024. "Detection of Current Transformer Saturation Based on Machine Learning," Mathematics, MDPI, vol. 12(3), pages 1-18, January.
    2. Sopheap Key & Gyu-Won Son & Soon-Ryul Nam, 2024. "Deep Learning-Based Algorithm for Internal Fault Detection of Power Transformers during Inrush Current at Distribution Substations," Energies, MDPI, vol. 17(4), pages 1-18, February.
    3. Shahriar Rahman Fahim & Subrata K. Sarker & S. M. Muyeen & Md. Rafiqul Islam Sheikh & Sajal K. Das, 2020. "Microgrid Fault Detection and Classification: Machine Learning Based Approach, Comparison, and Reviews," Energies, MDPI, vol. 13(13), pages 1-22, July.
    4. Sopheap Key & Chang-Sung Ko & Kwang-Jae Song & Soon-Ryul Nam, 2023. "Fast Detection of Current Transformer Saturation Using Stacked Denoising Autoencoders," Energies, MDPI, vol. 16(3), pages 1-16, February.
    5. Lefeng Cheng & Tao Yu, 2018. "Dissolved Gas Analysis Principle-Based Intelligent Approaches to Fault Diagnosis and Decision Making for Large Oil-Immersed Power Transformers: A Survey," Energies, MDPI, vol. 11(4), pages 1-69, April.
    6. Minghui Ou & Hua Wei & Yiyi Zhang & Jiancheng Tan, 2019. "A Dynamic Adam Based Deep Neural Network for Fault Diagnosis of Oil-Immersed Power Transformers," Energies, MDPI, vol. 12(6), pages 1-16, March.

    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:16:y:2023:i:24:p:7917-:d:1294091. 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.