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Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Author

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  • Minh-Tuan Nguyen

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

  • Viet-Hung Nguyen

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

  • Suk-Jun Yun

    (Prevention Diagnosis Team, Genad System, Naju 58296, Korea)

  • Yong-Hwa Kim

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

Abstract

The analysis of partial discharge (PD) signals has been identified as a standard diagnostic tool for monitoring the condition of different electrical apparatuses. This study proposes an approach to detecting PD patterns in gas-insulated switchgear (GIS) using a long short-term memory (LSTM) recurrent neural network (RNN). The proposed method uses phase-resolved PD (PRPD) signals as input, extracts low-level features, and finally, classifies faults in GIS. In the proposed method, LSTM networks can learn temporal dependencies directly from PRPD signals. Most existing models use support vector machines (SVMs) and mainly focus on improving feature representation and extraction manually to analyze PRPD signals. However, the proposed model captures important temporal features with the help of its low-level feature extraction capability from raw inputs. It outperforms conventional SVMs and achieves 96.74% classification accuracy for PRPDs in GIS.

Suggested Citation

  • Minh-Tuan Nguyen & Viet-Hung Nguyen & Suk-Jun Yun & Yong-Hwa Kim, 2018. "Recurrent Neural Network for Partial Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 11(5), pages 1-13, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1202-:d:145364
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    1. Abdullahi Abubakar Mas’ud & Ricardo Albarracín & Jorge Alfredo Ardila-Rey & Firdaus Muhammad-Sukki & Hazlee Azil Illias & Nurul Aini Bani & Abu Bakar Munir, 2016. "Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions," Energies, MDPI, vol. 9(8), pages 1-18, July.
    2. Abdullahi Abubakar Mas’ud & Jorge Alfredo Ardila-Rey & Ricardo Albarracín & Firdaus Muhammad-Sukki & Nurul Aini Bani, 2017. "Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources," Energies, MDPI, vol. 10(7), pages 1-20, July.
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    Cited by:

    1. Seokho Moon & Hansam Cho & Eunji Koh & Yong Sung Cho & Hyoung Lok Oh & Younghoon Kim & Seoung Bum Kim, 2022. "Remanufacturing Decision-Making for Gas Insulated Switchgear with Remaining Useful Life Prediction," Sustainability, MDPI, vol. 14(19), pages 1-13, September.
    2. Wang, Bo & Jia, Xiaoyu & Yang, Jian & Wang, Qiuwang, 2022. "Numerical study on temperature rise and structure optimization for a three-phase gas insulated switchgear busbar chamber," Energy, Elsevier, vol. 254(PC).
    3. Vo-Nguyen Tuyet-Doan & Tien-Tung Nguyen & Minh-Tuan Nguyen & Jong-Ho Lee & Yong-Hwa Kim, 2020. "Self-Attention Network for Partial-Discharge Diagnosis in Gas-Insulated Switchgear," Energies, MDPI, vol. 13(8), pages 1-16, April.
    4. Yanxin Wang & Jing Yan & Zhou Yang & Tingliang Liu & Yiming Zhao & Junyi Li, 2019. "Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network," Energies, MDPI, vol. 12(24), pages 1-19, December.
    5. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    6. Chin-Tan Lee & Shih-Cheng Horng, 2020. "Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree," Energies, MDPI, vol. 13(10), pages 1-19, May.
    7. Yaseen Ahmed Mohammed Alsumaidaee & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Kharudin Ali, 2022. "Review of Medium-Voltage Switchgear Fault Detection in a Condition-Based Monitoring System by Using Deep Learning," Energies, MDPI, vol. 15(18), pages 1-34, September.
    8. Sanuri Ishak & Chong Tak Yaw & Siaw Paw Koh & Sieh Kiong Tiong & Chai Phing Chen & Talal Yusaf, 2021. "Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine," Energies, MDPI, vol. 14(19), pages 1-21, October.
    9. 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.
    10. Sara Mantach & Ahmed Ashraf & Hamed Janani & Behzad Kordi, 2021. "A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set," Energies, MDPI, vol. 14(5), pages 1-16, March.
    11. Jiaying Deng & Wenhai Zhang & Xiaomei Yang, 2019. "Recognition and Classification of Incipient Cable Failures Based on Variational Mode Decomposition and a Convolutional Neural Network," Energies, MDPI, vol. 12(10), pages 1-16, May.
    12. Daria Wotzka & Wojciech Sikorski & Cyprian Szymczak, 2022. "Investigating the Capability of PD-Type Recognition Based on UHF Signals Recorded with Different Antennas Using Supervised Machine Learning," Energies, MDPI, vol. 15(9), pages 1-20, April.
    13. Sara Mantach & Abdulla Lutfi & Hamed Moradi Tavasani & Ahmed Ashraf & Ayman El-Hag & Behzad Kordi, 2022. "Deep Learning in High Voltage Engineering: A Literature Review," Energies, MDPI, vol. 15(14), pages 1-32, July.
    14. Xiu Zhou & Xutao Wu & Pei Ding & Xiuguang Li & Ninghui He & Guozhi Zhang & Xiaoxing Zhang, 2019. "Research on Transformer Partial Discharge UHF Pattern Recognition Based on Cnn-lstm," Energies, MDPI, vol. 13(1), pages 1-13, December.
    15. Sonia Barrios & David Buldain & María Paz Comech & Ian Gilbert & Iñaki Orue, 2019. "Partial Discharge Classification Using Deep Learning Methods—Survey of Recent Progress," Energies, MDPI, vol. 12(13), pages 1-16, June.

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