IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i7p6310-d1117514.html
   My bibliography  Save this article

A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants

Author

Listed:
  • Haixia Gu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China
    These authors contributed equally to this work.)

  • Gaojun Liu

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Jixue Li

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Hongyun Xie

    (State Key Laboratory of Nuclear Power Safety Monitoring Technology and Equipment, China Nuclear Power Engineering Company Ltd., Shenzhen 518000, China)

  • Hanguan Wen

    (School of Electric Power Engineering, South China University of Technology, Guangzhou 510641, China
    These authors contributed equally to this work.)

Abstract

Operators in the main control room of a nuclear power plant have a crucial role in supervising all operations, and any human error can be fatal. By providing operators with information regarding the future trends of plant safety-critical parameters based on their actions, human errors can be detected and prevented in a timely manner. This paper proposed a Sequence-to-Sequence (Seq2Seq)-based Long Short-Term Memory (LSTM) model to predict safety-critical parameters and their future trends. The PCTran was used to extract data for four typical faults and fault levels, and eighty-six parameters were selected as characteristic quantities. The training, validation, and testing sets were collected in a ratio of 13:3:1, and appropriate hyperparameters were used to construct the Seq2Seq neural network. Compared with conventional deep learning models, the results indicated that the proposed model could successfully solve the complex problem of the trend estimation of key system parameters under the influence of operator action factors in multiple abnormal operating conditions. It is believed that the proposed model can help operators reduce the risk of human-caused errors and diagnose potential accidents.

Suggested Citation

  • Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:7:p:6310-:d:1117514
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/7/6310/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/7/6310/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Cheng, Xu & Chen, Zhe, 2023. "An energy demand-side management and net metering decision framework," Energy, Elsevier, vol. 271(C).
    2. Niu, Zhibin & Wu, Junqi & Liu, Xiufeng & Huang, Lizhen & Nielsen, Per Sieverts, 2021. "Understanding energy demand behaviors through spatio-temporal smart meter data analysis," Energy, Elsevier, vol. 226(C).
    3. Byung-Ho Kim & Min-Jong Song & Yong-Sik Cho, 2022. "Safety Analysis of a Nuclear Power Plant against Unexpected Tsunamis," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    4. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    5. Bing Liu & Jichong Lei & Jinsen Xie & Jianliang Zhou, 2022. "Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning," Energies, MDPI, vol. 15(22), pages 1-15, November.
    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. Changan Ren & Jichong Lei & Jie Liu & Jun Hong & Hong Hu & Xiaoyong Fang & Cannan Yi & Zhiqiang Peng & Xiaohua Yang & Tao Yu, 2024. "Research on an Intelligent Fault Diagnosis Method for Small Modular Reactors," Energies, MDPI, vol. 17(16), pages 1-15, August.
    2. Xingyu Xiao & Jingang Liang & Jiejuan Tong & Haitao Wang, 2024. "Emergency Decision Support Techniques for Nuclear Power Plants: Current State, Challenges, and Future Trends," Energies, MDPI, vol. 17(10), pages 1-35, May.

    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. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    2. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    3. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    4. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Cheng, Xu & Chen, Zhe, 2023. "An energy demand-side management and net metering decision framework," Energy, Elsevier, vol. 271(C).
    5. Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2024. "Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)," Applied Energy, Elsevier, vol. 360(C).
    6. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    7. Cerna, Fernando V. & Dantas, Jamile T. & Naderi, Ehsan & Contreras, Javier, 2024. "Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control," Energy, Elsevier, vol. 294(C).
    8. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
    9. Shu, Lei & Mo, Yunjeong & Zhao, Dong, 2024. "Energy retrofits for smart and connected communities: Scopes and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    10. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    11. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    12. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    13. Alonso Pedrero, Raquel & Crespo del Granado, Pedro, 2023. "Assessing the impact of energy communities on retailers’ balancing positions in the power market," Energy, Elsevier, vol. 283(C).
    14. Xinyu Dai & Ming Yang & Jipu Wang & Zhihui Xu & Hanguan Wen, 2023. "Human Performance Detection Using Operator Action Log of Nuclear Power Plant," Energies, MDPI, vol. 16(4), pages 1-13, February.
    15. José Rubio-León & José Rubio-Cienfuegos & Cristian Vidal-Silva & Jesennia Cárdenas-Cobo & Vannessa Duarte, 2023. "Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
    16. Ahammed, Md. Tanvir & Khan, Imran, 2022. "Ensuring power quality and demand-side management through IoT-based smart meters in a developing country," Energy, Elsevier, vol. 250(C).
    17. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    18. Ahir, Rajesh K. & Chakraborty, Basab, 2023. "A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs," Energy, Elsevier, vol. 282(C).

    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:jsusta:v:15:y:2023:i:7:p:6310-:d:1117514. 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.