IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v326y2022ics0306261922012521.html
   My bibliography  Save this article

Online autonomous calibration of digital twins using machine learning with application to nuclear power plants

Author

Listed:
  • Song, Houde
  • Song, Meiqi
  • Liu, Xiaojing

Abstract

As a near-zero carbon emission energy source, nuclear energy plays an important role in the current world energy decarbonization scenario. Digital twin is a key technology for the continued development of nuclear energy applications. The digital twin requires real-time, high-precision simulations that are beyond the capabilities of current nuclear energy system simulation programs. Therefore, this study proposes an autonomous calibration method for the digital twin of nuclear power plants to compensate for the error in the results of the low accuracy digital twin that can run quickly to obtain higher accuracy results to meet both high accuracy and real-time requirements. The proposed method consists of offline and online stages. In the offline stage, digital twin simulations are first performed. The simulated data and corresponding measurements data (or real data) are used to build an error database, which will be used for the next step of data-driven model training. To reduce the complexity of calibration model, the error database samples are then grouped by clustering. Data-driven calibration models are built on each group based on the simulated data and errors. In the online stage, the digital twin runs in parallel with the nuclear power plant and receives real-time data. The calibration model is continuously updated using dynamic error database. The feasibility of the new proposed method has been demonstrated on measured data from the PKLIII B3.1 steam generator pipe rupture (SGTR) experiment. The results showed that the physical quantities such as pressure, temperature and mass flow rate were well calibrated during the 1000 s of parallel running. The R2 of all physical quantities including temperature, flow rate, and pressure are above 0.99.

Suggested Citation

  • Song, Houde & Song, Meiqi & Liu, Xiaojing, 2022. "Online autonomous calibration of digital twins using machine learning with application to nuclear power plants," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012521
    DOI: 10.1016/j.apenergy.2022.119995
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922012521
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119995?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    2. Chaube, Anshuman & Chapman, Andrew & Minami, Akari & Stubbins, James & Huff, Kathryn D., 2021. "The role of current and emerging technologies in meeting Japan’s mid- to long-term carbon reduction goals," Applied Energy, Elsevier, vol. 304(C).
    3. Hong, Sanghyun & Bradshaw, Corey J.A. & Brook, Barry W., 2014. "Nuclear power can reduce emissions and maintain a strong economy: Rating Australia’s optimal future electricity-generation mix by technologies and policies," Applied Energy, Elsevier, vol. 136(C), pages 712-725.
    4. Nian, Victor & Mignacca, Benito & Locatelli, Giorgio, 2022. "Policies toward net-zero: Benchmarking the economic competitiveness of nuclear against wind and solar energy," Applied Energy, Elsevier, vol. 320(C).
    5. Stewart, W.R. & Velez-Lopez, E. & Wiser, R. & Shirvan, K., 2021. "Economic solution for low carbon process heat: A horizontal, compact high temperature gas reactor," Applied Energy, Elsevier, vol. 304(C).
    6. Dong, Zhe & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2020. "Multilayer perception based reinforcement learning supervisory control of energy systems with application to a nuclear steam supply system," Applied Energy, Elsevier, vol. 259(C).
    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. Deng, Jiaolong & Guan, Chaoran & Sun, Yujie & Liu, Xiaojing & Zhang, Tengfei & He, Hui & Chai, Xiang, 2024. "Techno-economic analysis and dynamic performance evaluation of an integrated green concept based on concentrating solar power and a transportable heat pipe-cooled nuclear reactor," Energy, Elsevier, vol. 303(C).
    2. Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Zhao, Guanjia & Ma, Suxia, 2023. "Data-driven modeling-based digital twin of supercritical coal-fired boiler for metal temperature anomaly detection," Energy, Elsevier, vol. 278(PA).
    3. Song, Houde & Liu, Xiaojing & Song, Meiqi, 2023. "Comparative study of data-driven and model-driven approaches in prediction of nuclear power plants operating parameters," Applied Energy, Elsevier, vol. 341(C).
    4. Zhou, Shiqi & Lin, Meng & Huang, Shilong & Xiao, Kai, 2024. "Open set compound fault recognition method for nuclear power plant based on label mask weighted prototype learning," Applied Energy, Elsevier, vol. 369(C).

    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. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    2. Pedro Gomes da Cruz Filho & Danielle Devequi Gomes Nunes & Hayna Malta Santos & Alex Álisson Bandeira Santos & Bruna Aparecida Souza Machado, 2023. "From Patents to Progress: Genetic Algorithms in Harmonic Distortion Monitoring Technology," Energies, MDPI, vol. 16(24), pages 1-21, December.
    3. Mujammil Asdhiyoga Rahmanta & Rahmat Adiprasetya Al Hasibi & Handrea Bernando Tambunan & Ruly & Agussalim Syamsuddin & Indra Ardhanayudha Aditya & Benny Susanto, 2024. "Towards a Net Zero-Emission Electricity Generation System by Optimizing Renewable Energy Sources and Nuclear Power Plant," Energies, MDPI, vol. 17(8), pages 1-22, April.
    4. Nock, Destenie & Baker, Erin, 2019. "Holistic multi-criteria decision analysis evaluation of sustainable electric generation portfolios: New England case study," Applied Energy, Elsevier, vol. 242(C), pages 655-673.
    5. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    6. Bento, P.M.R. & Pombo, J.A.N. & Calado, M.R.A. & Mariano, S.J.P.S., 2018. "A bat optimized neural network and wavelet transform approach for short-term price forecasting," Applied Energy, Elsevier, vol. 210(C), pages 88-97.
    7. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    8. Alexandre Lucas & Konstantinos Pegios & Evangelos Kotsakis & Dan Clarke, 2020. "Price Forecasting for the Balancing Energy Market Using Machine-Learning Regression," Energies, MDPI, vol. 13(20), pages 1-16, October.
    9. Bobinaite Viktorija & Zuters Jānis, 2016. "Modelling Electricity Price Expectations in a Day-Ahead Market: A Case of Latvia," Economics and Business, Sciendo, vol. 29(1), pages 12-26, August.
    10. Wang, Yu & Gu, Jibao & Wu, Jianlin, 2020. "Explaining local residents’ acceptance of rebuilding nuclear power plants: The roles of perceived general benefit and perceived local benefit," Energy Policy, Elsevier, vol. 140(C).
    11. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    12. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    13. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
    14. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    15. Becerra-Fernandez, Mauricio & Sarmiento, Alfonso T. & Cardenas, Laura M., 2023. "Sustainability assessment of the solar energy supply chain in Colombia," Energy, Elsevier, vol. 282(C).
    16. Dounia El Bourakadi & Hiba Ramadan & Ali Yahyaouy & Jaouad Boumhidi, 2023. "A robust energy management approach in two-steps ahead using deep learning BiLSTM prediction model and type-2 fuzzy decision-making controller," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 645-667, December.
    17. Hui, Jiuwu & Lee, Yi-Kuen & Yuan, Jingqi, 2023. "Load following control of a PWR with load-dependent parameters and perturbations via fixed-time fractional-order sliding mode and disturbance observer techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    19. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    20. Zhe Dong & Zhonghua Cheng & Yunlong Zhu & Xiaojin Huang & Yujie Dong & Zuoyi Zhang, 2023. "Review on the Recent Progress in Nuclear Plant Dynamical Modeling and Control," Energies, MDPI, vol. 16(3), pages 1-19, February.

    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:eee:appene:v:326:y:2022:i:c:s0306261922012521. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.