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A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems

Author

Listed:
  • Sun-Bin Kim

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Korea)

  • Vattanak Sok

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Korea)

  • Sang-Hee Kang

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Korea)

  • Nam-Ho Lee

    (Korea Electric Power Research Institute, Daejeon 34056, Korea)

  • Soon-Ryul Nam

    (Department of Electrical Engineering, Myongji University, Yongin 17058, Korea)

Abstract

The purpose of this paper is to remove the exponentially decaying DC offset in fault current waveforms using a deep neural network (DNN), even under harmonics and noise distortion. The DNN is implemented using the TensorFlow library based on Python. Autoencoders are utilized to determine the number of neurons in each hidden layer. Then, the number of hidden layers is experimentally decided by comparing the performance of DNNs with different numbers of hidden layers. Once the optimal DNN size has been determined, intensive training is performed using both the supervised and unsupervised training methodologies. Through various case studies, it was verified that the DNN is immune to harmonics, noise distortion, and variation of the time constant of the DC offset. In addition, it was found that the DNN can be applied to power systems with different voltage levels.

Suggested Citation

  • Sun-Bin Kim & Vattanak Sok & Sang-Hee Kang & Nam-Ho Lee & Soon-Ryul Nam, 2019. "A Study on Deep Neural Network-Based DC Offset Removal for Phase Estimation in Power Systems," Energies, MDPI, vol. 12(9), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1619-:d:226779
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    References listed on IDEAS

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    1. Jae Suk Lee & Seon-Hwan Hwang, 2018. "DC Offset Error Compensation Algorithm for PR Current Control of a Single-Phase Grid-Tied Inverter," Energies, MDPI, vol. 11(9), pages 1-13, September.
    2. Xiaoyao Huang & Tianbin Hu & Chengjin Ye & Guanhua Xu & Xiaojian Wang & Liangjin Chen, 2019. "Electric Load Data Compression and Classification Based on Deep Stacked Auto-Encoders," Energies, MDPI, vol. 12(4), pages 1-17, February.
    3. Wenping Hu & Jifeng Liang & Yitao Jin & Fuzhang Wu & Xiaowei Wang & Ersong Chen, 2018. "Online Evaluation Method for Low Frequency Oscillation Stability in a Power System Based on Improved XGboost," Energies, MDPI, vol. 11(11), pages 1-18, November.
    4. Myoungsoo Kim & Wonik Choi & Youngjun Jeon & Ling Liu, 2019. "A Hybrid Neural Network Model for Power Demand Forecasting," Energies, MDPI, vol. 12(5), pages 1-17, March.
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    Cited by:

    1. Vattanak Sok & Sun-Woo Lee & Sang-Hee Kang & Soon-Ryul Nam, 2022. "Deep Neural Network-Based Removal of a Decaying DC Offset in Less Than One Cycle for Digital Relaying," Energies, MDPI, vol. 15(7), pages 1-14, April.
    2. Raffay Rizwan & Jehangir Arshad & Ahmad Almogren & Mujtaba Hussain Jaffery & Adnan Yousaf & Ayesha Khan & Ateeq Ur Rehman & Muhammad Shafiq, 2021. "Implementation of ANN-Based Embedded Hybrid Power Filter Using HIL-Topology with Real-Time Data Visualization through Node-RED," Energies, MDPI, vol. 14(21), pages 1-33, November.
    3. Sina Mohammadi & Amin Mahmoudi & Solmaz Kahourzade & Amirmehdi Yazdani & GM Shafiullah, 2022. "Decaying DC Offset Current Mitigation in Phasor Estimation Applications: A Review," Energies, MDPI, vol. 15(14), pages 1-33, 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.

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