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An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Author

Listed:
  • Muhammad Ali

    (Department of Electrical Engineering, Myongji University, Yongin 449-728, Korea)

  • Dae-Hee Son

    (Department of Electrical Engineering, Myongji University, Yongin 449-728, Korea)

  • Sang-Hee Kang

    (Department of Electrical Engineering, Myongji University, Yongin 449-728, Korea)

  • Soon-Ryul Nam

    (Department of Electrical Engineering, Myongji University, Yongin 449-728, Korea)

Abstract

Current transformer (CT) saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL) methods have brought a subversive revolution in the field of artificial intelligence (AI). This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs) to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1830-:d:118430
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    References listed on IDEAS

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    1. BeomJun Park & Jin Hur, 2017. "Accurate Short-Term Power Forecasting of Wind Turbines: The Case of Jeju Island’s Wind Farm," Energies, MDPI, vol. 10(6), pages 1-15, June.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.

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