IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v238y2022ipas0360544221019071.html
   My bibliography  Save this article

A performance evaluation framework for deep peak shaving of the CFB boiler unit based on the DBN-LSSVM algorithm

Author

Listed:
  • Hong, Feng
  • Wang, Rui
  • Song, Jie
  • Gao, Mingming
  • Liu, Jizhen
  • Long, Dongteng

Abstract

Under such a circumstance that the scale of renewable power connected into grids increases companied with more fluctuation, the flexibility and stability in power generation have been focus. Circulating fluidized bed (CFB) has unique merits in deep peak shaving, but its operation presents multi-influencing factors and multi-mode characteristics, which makes it very difficult to monitor the operation state. Toward this end, a novel performance evaluation framework has been proposed. The proposed framework contains two main parts: deep feature extraction conducted by deep belief networks (DBN), connecting with performance status classification by least square support vector machine (LSSVM). In this framework, massive operation data detected by sensors and reference status labels were entered into DBN for dimension reduction and feature extraction in a semi-supervised way. LSSVM finished the status classification based on these features. The final classification results are processed by DBN and LSSVM successively, which can not only make full use of the multidimensional parameters of CFB, but also avoid the influence of multimode of CFB. Besides, some comparations of the case study are conducted and analysed respectively to verify the efficiency and accuracy of the performance evaluation framework.

Suggested Citation

  • Hong, Feng & Wang, Rui & Song, Jie & Gao, Mingming & Liu, Jizhen & Long, Dongteng, 2022. "A performance evaluation framework for deep peak shaving of the CFB boiler unit based on the DBN-LSSVM algorithm," Energy, Elsevier, vol. 238(PA).
  • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221019071
    DOI: 10.1016/j.energy.2021.121659
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221019071
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.121659?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. Hong, Feng & Chen, Jiyu & Wang, Rui & Long, Dongteng & Yu, Haoyang & Gao, Mingming, 2021. "Realization and performance evaluation for long-term low-load operation of a CFB boiler unit," Energy, Elsevier, vol. 214(C).
    2. Gu, Yujiong & Xu, Jing & Chen, Dongchao & Wang, Zhong & Li, Qianqian, 2016. "Overall review of peak shaving for coal-fired power units in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 723-731.
    3. Nikula, Riku-Pekka & Ruusunen, Mika & Leiviskä, Kauko, 2016. "Data-driven framework for boiler performance monitoring," Applied Energy, Elsevier, vol. 183(C), pages 1374-1388.
    4. Kubik, M.L. & Coker, P.J. & Barlow, J.F., 2015. "Increasing thermal plant flexibility in a high renewables power system," Applied Energy, Elsevier, vol. 154(C), pages 102-111.
    5. Fan Zhang & Yali Xue & Donghai Li & Zhenlong Wu & Ting He, 2019. "On the Flexible Operation of Supercritical Circulating Fluidized Bed: Burning Carbon Based Decentralized Active Disturbance Rejection Control," Energies, MDPI, vol. 12(6), pages 1-18, 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. Lei Han & Lingmei Wang & Hairui Yang & Chengzhen Jia & Enlong Meng & Yushan Liu & Shaoping Yin, 2023. "Optimization of Circulating Fluidized Bed Boiler Combustion Key Control Parameters Based on Machine Learning," Energies, MDPI, vol. 16(15), pages 1-23, July.
    2. Hou, Guolian & Gong, Linjuan & Hu, Bo & Huang, Ting & Su, Huilin & Huang, Congzhi & Zhou, Guiping & Wang, Shunjiang, 2022. "Flexibility oriented adaptive modeling of combined heat and power plant under various heat-power coupling conditions," Energy, Elsevier, vol. 242(C).
    3. Hong, Feng & Zhao, Yuzheng & Ji, Weiming & Fang, Fang & Hao, Junhong & Yang, Zhenyong & Kang, Jingqiu & Chen, Lei & Liu, Jizhen, 2024. "A feature-state observer and suppression control for generation-side low-frequency oscillation of thermal power units," Applied Energy, Elsevier, vol. 354(PA).
    4. Du, Pei & Yang, Dongchuan & Li, Yanzhao & Wang, Jianzhou, 2024. "An innovative interpretable combined learning model for wind speed forecasting," Applied Energy, Elsevier, vol. 358(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. Jianjun Wang & Jikun Huo & Shuo Zhang & Yun Teng & Li Li & Taoya Han, 2021. "Flexibility Transformation Decision-Making Evaluation of Coal-Fired Thermal Power Units Deep Peak Shaving in China," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    2. Chunning Na & Huan Pan & Yuhong Zhu & Jiahai Yuan & Lixia Ding & Jungang Yu, 2019. "The Flexible Operation of Coal Power and Its Renewable Integration Potential in China," Sustainability, MDPI, vol. 11(16), pages 1-17, August.
    3. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    4. Dianfa Wu & Zhiping Yang & Ningling Wang & Chengzhou Li & Yongping Yang, 2018. "An Integrated Multi-Criteria Decision Making Model and AHP Weighting Uncertainty Analysis for Sustainability Assessment of Coal-Fired Power Units," Sustainability, MDPI, vol. 10(6), pages 1-27, May.
    5. Antti Alahäivälä & Juha Kiviluoma & Jyrki Leino & Matti Lehtonen, 2017. "System-Level Value of a Gas Engine Power Plant in Electricity and Reserve Production," Energies, MDPI, vol. 10(7), pages 1-13, July.
    6. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    7. Andrychowicz, Mateusz & Olek, Blazej & Przybylski, Jakub, 2017. "Review of the methods for evaluation of renewable energy sources penetration and ramping used in the Scenario Outlook and Adequacy Forecast 2015. Case study for Poland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 703-714.
    8. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    9. Erik Rosado-Tamariz & Miguel A. Zuniga-Garcia & Alfonso Campos-Amezcua & Rafael Batres, 2020. "A Framework for the Synthesis of Optimum Operating Profiles Based on Dynamic Simulation and a Micro Genetic Algorithm," Energies, MDPI, vol. 13(3), pages 1-23, February.
    10. Marco Badami & Gabriele Fambri & Salvatore Mancò & Mariapia Martino & Ioannis G. Damousis & Dimitrios Agtzidis & Dimitrios Tzovaras, 2019. "A Decision Support System Tool to Manage the Flexibility in Renewable Energy-Based Power Systems," Energies, MDPI, vol. 13(1), pages 1-16, December.
    11. Hui-Yu Jin & Yang Chen, 2023. "First-Order Linear Active Disturbance Rejection Control for Turbofan Engines," Energies, MDPI, vol. 16(6), pages 1-17, March.
    12. Zhang, Hongfu & Gao, Mingming & Fan, Haohao & Zhang, Kaiping & Zhang, Jiahui, 2022. "A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units," Energy, Elsevier, vol. 241(C).
    13. Zhang, Youjun & Hao, Junhong & Ge, Zhihua & Zhang, Fuxiang & Du, Xiaoze, 2021. "Optimal clean heating mode of the integrated electricity and heat energy system considering the comprehensive energy-carbon price," Energy, Elsevier, vol. 231(C).
    14. Ming, Zeng & Ping, Zhang & Shunkun, Yu & Hui, Liu, 2017. "Overall review of the overcapacity situation of China’s thermal power industry: Status quo, policy analysis and suggestions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 768-774.
    15. Pavić, Ivan & Capuder, Tomislav & Kuzle, Igor, 2016. "Low carbon technologies as providers of operational flexibility in future power systems," Applied Energy, Elsevier, vol. 168(C), pages 724-738.
    16. Geng, Xinmin & Zhou, Ye & Zhao, Weiqiang & Shi, Li & Chen, Diyi & Bi, Xiaojian & Xu, Beibei, 2024. "Pricing ancillary service of a Francis hydroelectric generating system to promote renewable energy integration in a clean energy base: Tariff compensation of deep peak regulation," Renewable Energy, Elsevier, vol. 226(C).
    17. Ding, Jie & Xu, Yujie & Chen, Haisheng & Sun, Wenwen & Hu, Shan & Sun, Shuang, 2019. "Value and economic estimation model for grid-scale energy storage in monopoly power markets," Applied Energy, Elsevier, vol. 240(C), pages 986-1002.
    18. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2017. "Securing highly penetrated wind energy systems using linearized transmission switching mechanism," Applied Energy, Elsevier, vol. 190(C), pages 1207-1220.
    19. Yan, Hui & Liu, Ming & Wang, Zhu & Zhang, Kezhen & Chong, Daotong & Yan, Junjie, 2023. "Flexibility enhancement of solar-aided coal-fired power plant under different direct normal irradiance conditions," Energy, Elsevier, vol. 262(PA).
    20. Zhang, Yuning & Tang, Ningning & Niu, Yuguang & Du, Xiaoze, 2016. "Wind energy rejection in China: Current status, reasons and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 322-344.

    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:energy:v:238:y:2022:i:pa:s0360544221019071. 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.journals.elsevier.com/energy .

    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.