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Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion

Author

Listed:
  • Zhenhua Li

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Xingxin Chen

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Lin Wu

    (State Grid Hubei Power Company Technology Training Center (Wuhan Electric Power Technical College), Wuhan 430014, China)

  • Abu-Siada Ahmed

    (Discipline of Electrical and Computer Engineering, Curtin University, Perth 6000, Australia)

  • Tao Wang

    (State Grid Hubei Power Company Technology Training Center (Wuhan Electric Power Technical College), Wuhan 430014, China)

  • Yujie Zhang

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Hongbin Li

    (School of Electrical and Electronic Engineering, Huazhong University of School and Technology, Wuhan 430074, China)

  • Zhenxing Li

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Yanchun Xu

    (College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China)

  • Yue Tong

    (Wuhan Branch Chinese Academy of Sciences, Wuhan 430071, China)

Abstract

Air-core coil current transformer is a key piece of equipment in the digital substation development. However, it is more vulnerable to various faults when compared with the traditional electromagnetic current transformer. Aiming at understanding the effect of various parameters on the performance of the air-core coil current transformer, this paper investigates the influence of these factors using the maximum information coefficient. The interference mechanism of influencing factors on the transformer error is also analyzed. Finally, the Stacking model fusion algorithm is used to predict transformer errors. The developed base model consists of deep learning, integrated learning and traditional learning algorithms. Compared with gated recurrent units and extreme gradient boosting algorithms, the prediction model based on stacking model fusion algorithm proposed in this paper features higher accuracy and reliability which helps improve the performance and safety of future digital substations.

Suggested Citation

  • Zhenhua Li & Xingxin Chen & Lin Wu & Abu-Siada Ahmed & Tao Wang & Yujie Zhang & Hongbin Li & Zhenxing Li & Yanchun Xu & Yue Tong, 2021. "Error Analysis of Air-Core Coil Current Transformer Based on Stacking Model Fusion," Energies, MDPI, vol. 14(7), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:1912-:d:526994
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    References listed on IDEAS

    as
    1. Bingchun Liu & Chuanchuan Fu & Arlene Bielefield & Yan Quan Liu, 2017. "Forecasting of Chinese Primary Energy Consumption in 2021 with GRU Artificial Neural Network," Energies, MDPI, vol. 10(10), pages 1-15, September.
    2. Jun Jiang & Mingxin Zhao & Chaohai Zhang & Min Chen & Haojun Liu & Ricardo Albarracín, 2018. "Partial Discharge Analysis in High-Frequency Transformer Based on High-Frequency Current Transducer," Energies, MDPI, vol. 11(8), pages 1-13, August.
    3. Gang Yao & Shuxiu Pang & Tingting Ying & Mohamed Benbouzid & Mourad Ait-Ahmed & Mohamed Fouad Benkhoris, 2020. "VPSO-SVM-Based Open-Circuit Faults Diagnosis of Five-Phase Marine Current Generator Sets," Energies, MDPI, vol. 13(22), pages 1-28, November.
    4. Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020. "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    5. Guanchen Liu & Peng Zhao & Yang Qin & Mingmin Zhao & Zhichao Yang & Henglin Chen, 2020. "Electromagnetic Immunity Performance of Intelligent Electronic Equipment in Smart Substation’s Electromagnetic Environment," Energies, MDPI, vol. 13(5), pages 1-19, March.
    6. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
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    Cited by:

    1. Ernest Stano & Piotr Kaczmarek & Michal Kaczmarek, 2022. "Understanding the Frequency Characteristics of Current Error and Phase Displacement of the Corrected Inductive Current Transformer," Energies, MDPI, vol. 15(15), pages 1-16, July.

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