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Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid

Author

Listed:
  • Gu Xiong

    (China Electric Power Research Institute, Wuhan 430000, China)

  • Krzysztof Przystupa

    (Department of Automation, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland)

  • Yao Teng

    (China Electric Power Research Institute, Wuhan 430000, China)

  • Wang Xue

    (China Electric Power Research Institute, Wuhan 430000, China)

  • Wang Huan

    (China Electric Power Research Institute, Wuhan 430000, China)

  • Zhou Feng

    (State Grid Chongqing Electric Power Company Marketing Service Center, Chongqing 400015, China)

  • Xiang Qiong

    (China Electric Power Research Institute, Wuhan 430000, China)

  • Chunzhi Wang

    (School of Computer Science, Hubei University of Technology, Wuhan 430000, China)

  • Mikołaj Skowron

    (Department of Electrical and Power Engineering, AGH University of Science and Technology, A. Mickiewicza 30, 30-059 Krakow, Poland)

  • Orest Kochan

    (School of Computer Science, Hubei University of Technology, Wuhan 430000, China
    Department of Telecommunications, Lviv Polytechnic National University, Bandery 12, 79013 Lviv, Ukraine)

  • Mykola Beshley

    (Department of Telecommunications, Lviv Polytechnic National University, Bandery 12, 79013 Lviv, Ukraine)

Abstract

With the development of smart power grids, electronic transformers have been widely used to monitor the online status of power grids. However, electronic transformers have the drawback of poor long-term stability, leading to a requirement for frequent measurement. Aiming to monitor the online status frequently and conveniently, we proposed an attention mechanism-optimized Seq2Seq network to predict the error state of transformers, which combines an attention mechanism, Seq2Seq network, and bidirectional long short-term memory networks to mine the sequential information from online monitoring data of electronic transformers. We implemented the proposed method on the monitoring data of electronic transformers in a certain electric field. Experiments showed that our proposed attention mechanism-optimized Seq2Seq network has high accuracy in the aspect of error prediction.

Suggested Citation

  • Gu Xiong & Krzysztof Przystupa & Yao Teng & Wang Xue & Wang Huan & Zhou Feng & Xiang Qiong & Chunzhi Wang & Mikołaj Skowron & Orest Kochan & Mykola Beshley, 2021. "Online Measurement Error Detection for the ElectronicTransformer in a Smart Grid," Energies, MDPI, vol. 14(12), pages 1-18, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3551-:d:575047
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    References listed on IDEAS

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    1. Akhil Joseph & Patil Balachandra, 2020. "Energy Internet, the Future Electricity System: Overview, Concept, Model Structure, and Mechanism," Energies, MDPI, vol. 13(16), pages 1-26, August.
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    Cited by:

    1. Donatas Gurauskis & Krzysztof Przystupa & Artūras Kilikevičius & Mikołaj Skowron & Matijošius Jonas & Joanna Michałowska & Kristina Kilikevičienė, 2022. "Performance Analysis of an Experimental Linear Encoder’s Reading Head under Different Mounting and Dynamic Conditions," Energies, MDPI, vol. 15(16), pages 1-13, August.

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