IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i9p1619-d226779.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/9/1619/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/9/1619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. 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. Antonio E. Saldaña-González & Andreas Sumper & Mònica Aragüés-Peñalba & Miha Smolnikar, 2020. "Advanced Distribution Measurement Technologies and Data Applications for Smart Grids: A Review," Energies, MDPI, vol. 13(14), pages 1-34, July.
    3. Shubhra Paul & Lauren B. Davis, 2022. "An ensemble forecasting model for predicting contribution of food donors based on supply behavior," Annals of Operations Research, Springer, vol. 319(1), pages 1-29, December.
    4. Mariusz Kmiecik, 2022. "Logistics Coordination Based on Inventory Management and Transportation Planning by Third-Party Logistics (3PL)," Sustainability, MDPI, vol. 14(13), pages 1-19, July.
    5. Carolina Deina & João Lucas Ferreira dos Santos & Lucas Henrique Biuk & Mauro Lizot & Attilio Converti & Hugo Valadares Siqueira & Flavio Trojan, 2023. "Forecasting Electricity Demand by Neural Networks and Definition of Inputs by Multi-Criteria Analysis," Energies, MDPI, vol. 16(4), pages 1-24, February.
    6. Andreea Valeria Vesa & Tudor Cioara & Ionut Anghel & Marcel Antal & Claudia Pop & Bogdan Iancu & Ioan Salomie & Vasile Teodor Dadarlat, 2020. "Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs," Sustainability, MDPI, vol. 12(4), pages 1-23, February.
    7. Raneen Younis & Andreas Reinhardt, 2020. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures," Energies, MDPI, vol. 13(12), pages 1-19, June.
    8. Jeng-Wei Lin & Shih-wei Liao & Fang-Yie Leu, 2019. "Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction," Energies, MDPI, vol. 12(13), pages 1-20, June.
    9. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
    10. Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
    11. Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
    12. Chaturvedi, Shobhit & Rajasekar, Elangovan & Natarajan, Sukumar & McCullen, Nick, 2022. "A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India," Energy Policy, Elsevier, vol. 168(C).
    13. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
    14. Krzysztof Lowczowski & Jozef Lorenc & Andrzej Tomczewski & Zbigniew Nadolny & Jozef Zawodniak, 2020. "Monitoring of MV Cable Screens, Cable Joints and Earthing Systems Using Cable Screen Current Measurements," Energies, MDPI, vol. 13(13), pages 1-28, July.
    15. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
    16. Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
    17. Gwiman Bak & Youngchul Bae, 2020. "Predicting the Amount of Electric Power Transaction Using Deep Learning Methods," Energies, MDPI, vol. 13(24), pages 1-30, December.
    18. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.

    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:12:y:2019:i:9:p:1619-:d:226779. 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.