IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3474-d573690.html
   My bibliography  Save this article

Soft Sensor of Heating Extraction Steam Flow Rate Based on Frequency Complementary Information Fusion for CHP Plant

Author

Listed:
  • Liang Tian

    (School of Control and Computer Engineering, North China Electric Power University, Yonghua North Street, No.619, Baoding 071003, China)

  • Xinping Liu

    (School of Control and Computer Engineering, North China Electric Power University, Yonghua North Street, No.619, Baoding 071003, China)

  • Huanhuan Luo

    (State Grid Liaoning Electric Power Company Limited, Shenyang 110006, China)

  • Tuoyu Deng

    (School of Control and Computer Engineering, North China Electric Power University, Yonghua North Street, No.619, Baoding 071003, China)

  • Jizhen Liu

    (School of Control and Computer Engineering, North China Electric Power University, Yonghua North Street, No.619, Baoding 071003, China)

  • Guiping Zhou

    (Electric Power Research Institute of State Grid Liaoning Electric Power Co., Ltd., Shenyang 110006, China)

  • Tianting Zhang

    (School of Electric and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

Heating extraction steam (HEXTR) flow rate is the key parameter to determine the heat load of a combined heat and power (CHP) plant and the safe operation area of the steam turbine of CHP plant. Due to the difficulty of direct measurement, a soft measurement method of this flow rate is proposed. First, three calculation methods based on different principles are given: the Flügel formula of the steam turbine method, the butterfly valve flow characteristics method, and the improvement of heat balance characteristic of the turbine method. Then, a soft-sensing method through frequency complementary information fusion is proposed to combine the advantages of the three methods. The specific fusion algorithm uses Flügel formula of the turbine as a static model, the heat balance characteristic of the turbine to correct the coefficient in the model, and the butterfly valve characteristic to realize dynamic compensation. Finally, the proposed soft sensor is applied in the monitoring system of a typical 330 MW CHP plant. The actual operating data shows that the relative static measurement error of the soft sensor is less than 1% and the dynamic response is as fast as power load change.

Suggested Citation

  • Liang Tian & Xinping Liu & Huanhuan Luo & Tuoyu Deng & Jizhen Liu & Guiping Zhou & Tianting Zhang, 2021. "Soft Sensor of Heating Extraction Steam Flow Rate Based on Frequency Complementary Information Fusion for CHP Plant," Energies, MDPI, vol. 14(12), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3474-:d:573690
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3474/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3474/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kortela, J. & Jämsä-Jounela, S.-L., 2014. "Model predictive control utilizing fuel and moisture soft-sensors for the BioPower 5 combined heat and power (CHP) plant," Applied Energy, Elsevier, vol. 131(C), pages 189-200.
    2. Trojan, Marcin & Taler, Dawid & Dzierwa, Piotr & Taler, Jan & Kaczmarski, Karol & Wrona, Jan, 2019. "The use of pressure hot water storage tanks to improve the energy flexibility of the steam power unit," Energy, Elsevier, vol. 173(C), pages 926-936.
    3. Liang Tian & Yunlei Xie & Bo Hu & Xinping Liu & Tuoyu Deng & Huanhuan Luo & Fengqiang Li, 2019. "A Deep Peak Regulation Auxiliary Service Bidding Strategy for CHP Units Based on a Risk-Averse Model and District Heating Network Energy Storage," Energies, MDPI, vol. 12(17), pages 1-27, August.
    Full references (including those not matched with items on IDEAS)

    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. Yan Zhang & Quan Lyu & Yang Li & Na Zhang & Lijun Zheng & Haoyan Gong & Hui Sun, 2020. "Research on Down-Regulation Cost of Flexible Combined Heat Power Plants Participating in Real-Time Deep Down-Regulation Market," Energies, MDPI, vol. 13(4), pages 1-17, February.
    2. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    3. 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).
    4. Yanjuan Yu & Guohua Zhou & Kena Wu & Cheng Chen & Qiang Bian, 2023. "Optimal Configuration of Power-to-Heat Equipment Considering Peak-Shaving Ancillary Service Market," Energies, MDPI, vol. 16(19), pages 1-18, September.
    5. Alexandre Boriouchkine & Sirkka-Liisa Jämsä-Jounela, 2016. "Simplification of a Mechanistic Model of Biomass Combustion for On-Line Computations," Energies, MDPI, vol. 9(9), pages 1-25, September.
    6. Yunhai Zhou & Shengkai Guo & Fei Xu & Dai Cui & Weichun Ge & Xiaodong Chen & Bo Gu, 2020. "Multi-Time Scale Optimization Scheduling Strategy for Combined Heat and Power System Based on Scenario Method," Energies, MDPI, vol. 13(7), pages 1-18, April.
    7. Taler, Jan & Trojan, Marcin & Dzierwa, Piotr & Kaczmarski, Karol & Węglowski, Bohdan & Taler, Dawid & Zima, Wiesław & Grądziel, Sławomir & Ocłoń, Paweł & Sobota, Tomasz & Rerak, Monika & Jaremkiewicz,, 2023. "The flexible boiler operation in a wide range of load changes with considering the strength and environmental restrictions," Energy, Elsevier, vol. 263(PB).
    8. Annelies Vandermeulen & Ina De Jaeger & Tijs Van Oevelen & Dirk Saelens & Lieve Helsen, 2020. "Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility," Energies, MDPI, vol. 13(23), pages 1-25, November.
    9. Biyun Chen & Yanni Chen & Bin Li & Yun Zhu & Chi Zhang, 2022. "An Optimal Dispatching Model for Integrated Energy Microgrid Considering the Reliability Principal–Agent Contract," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    10. Palash Sarkar & Jukka Kortela & Alexandre Boriouchkine & Elena Zattoni & Sirkka-Liisa Jämsä-Jounela, 2017. "Data-Reconciliation Based Fault-Tolerant Model Predictive Control for a Biomass Boiler," Energies, MDPI, vol. 10(2), pages 1-14, February.
    11. Böhler, Lukas & Fallmann, Markus & Görtler, Gregor & Krail, Jürgen & Schittl, Florian & Kozek, Martin, 2021. "Emission limited model predictive control of a small-scale biomass furnace," Applied Energy, Elsevier, vol. 285(C).
    12. Wang, Di & Liu, Deying & Wang, Chaonan & Zhou, Yunlong & Li, Xiaoli & Yang, Mei, 2022. "Flexibility improvement method of coal-fired thermal power plant based on the multi-scale utilization of steam turbine energy storage," Energy, Elsevier, vol. 239(PD).
    13. Böhler, Lukas & Görtler, Gregor & Krail, Jürgen & Kozek, Martin, 2019. "Carbon monoxide emission models for small-scale biomass combustion of wooden pellets," Applied Energy, Elsevier, vol. 254(C).
    14. Li, Yuanzheng & Huang, Jingjing & Liu, Yun & Zhao, Tianyang & Zhou, Yue & Zhao, Yong & Yuen, Chau, 2022. "Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study," Energy, Elsevier, vol. 254(PA).
    15. Dzierwa, Piotr & Taler, Jan & Peret, Patryk & Taler, Dawid & Trojan, Marcin, 2022. "Transient CFD simulation of charging hot water tank," Energy, Elsevier, vol. 239(PC).
    16. Ferrari, Mario L., 2015. "Advanced control approach for hybrid systems based on solid oxide fuel cells," Applied Energy, Elsevier, vol. 145(C), pages 364-373.
    17. Wang, Zhu & Liu, Ming & Yan, Hui & Yan, Junjie, 2022. "Optimization on coordinate control strategy assisted by high-pressure extraction steam throttling to achieve flexible and efficient operation of thermal power plants," Energy, Elsevier, vol. 244(PA).
    18. Bravo, Julio & Abdulridha, Ahmed & Wang, Shuoyu & Matrone, Dominic & Yao, Zheng & Neti, Sudhakar & Naito, Clay & Quiel, Spencer & Suleiman, Muhannad & Romero, Carlos, 2023. "Design and experimental testing of a 150 kWh thermal battery using thermosiphons embedded in a concrete matrix for power plant flexible operation," Energy, Elsevier, vol. 277(C).
    19. Taler, Dawid & Dzierwa, Piotr & Kaczmarski, Karol & Taler, Jan, 2021. "Optimisation of heating and cooling of pressure thick-walled components operating in the saturated steam area," Energy, Elsevier, vol. 231(C).
    20. Böhler, Lukas & Krail, Jürgen & Görtler, Gregor & Kozek, Martin, 2020. "Fuzzy model predictive control for small-scale biomass combustion furnaces," Applied Energy, Elsevier, vol. 276(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:jeners:v:14:y:2021:i:12:p:3474-:d:573690. 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.