IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i2p231-d197304.html
   My bibliography  Save this article

Maximum Sensitivity-Constrained Data-Driven Active Disturbance Rejection Control with Application to Airflow Control in Power Plant

Author

Listed:
  • Ting He

    (State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Zhenlong Wu

    (State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Rongqi Shi

    (Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China)

  • Donghai Li

    (State Key Lab of Power Systems, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China)

  • Li Sun

    (Key Lab of Energy Thermal Conversion and Control of Ministry of Education, Southeast University, Nanjing 210096, China)

  • Lingmei Wang

    (Automation Department, Shanxi University, Taiyuan 030013, China)

  • Song Zheng

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China)

Abstract

The increasing energy demand and the changing of energy structure have imposed higher requirements on the conventional large-scale power plants control. Complexity of the power plant processes and the frequent change of operation condition make the accurate physical models hard to obtain for control design. To this end, a data-driven control strategy, the active disturbance rejection control (ADRC) has received much attention for the estimation and mitigation of uncertain dynamics beyond the canonical form of cascaded integrators. However, the robustness of ADRC is seldom discussed in a quantitative manner. In this study, the maximum sensitivity is used to evaluate and then constrain the robustness of ADRC applied to high-order processes. Firstly, by using the new idea of the vertical asymptote of the Nyquist curve, a preliminary one-parameter-tuning method is developed. Secondly, a quantitative relationship between the maximum sensitivity and the tuning parameter is established using optimization methods. Then, the feasibility and effectiveness of the proposed method is initially verified in the total air flow control of a power plant simulator. Finally, field tests on the secondary airflow control in a 330 MWe circulating fluidized bed confirm the merit of the proposed maximum sensitivity-constrained ADRC tuning.

Suggested Citation

  • Ting He & Zhenlong Wu & Rongqi Shi & Donghai Li & Li Sun & Lingmei Wang & Song Zheng, 2019. "Maximum Sensitivity-Constrained Data-Driven Active Disturbance Rejection Control with Application to Airflow Control in Power Plant," Energies, MDPI, vol. 12(2), pages 1-23, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:2:p:231-:d:197304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/2/231/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/2/231/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sun, Li & Shen, Jiong & Hua, Qingsong & Lee, Kwang Y., 2018. "Data-driven oxygen excess ratio control for proton exchange membrane fuel cell," Applied Energy, Elsevier, vol. 231(C), pages 866-875.
    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. Youjie Ma & Long Tao & Xuesong Zhou & Wei Li & Xueqi Shi, 2019. "Analysis and Control of Wind Power Grid Integration Based on a Permanent Magnet Synchronous Generator Using a Fuzzy Logic System with Linear Extended State Observer," Energies, MDPI, vol. 12(15), pages 1-19, July.
    2. Youjie Ma & Faqing Zhao & Xuesong Zhou & Mao Liu & Bao Yang, 2019. "DC Side Bus Voltage Control of Wind Power Grid-Connected Inverter Based on Second-Order Linear Active Disturbance Rejection Control," Energies, MDPI, vol. 12(22), pages 1-20, November.
    3. 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.
    4. Gengjin Shi & Zhenlong Wu & Jian Guo & Donghai Li & Yanjun Ding, 2020. "Superheated Steam Temperature Control Based on a Hybrid Active Disturbance Rejection Control," Energies, MDPI, vol. 13(7), pages 1-26, April.
    5. Raul-Cristian Roman & Radu-Emil Precup & Emil M. Petriu & Florin Dragan, 2019. "Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems," Energies, MDPI, vol. 12(8), pages 1-19, April.
    6. 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.

    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. Sun, Li & Sun, Wen & You, Fengqi, 2020. "Core temperature modelling and monitoring of lithium-ion battery in the presence of sensor bias," Applied Energy, Elsevier, vol. 271(C).
    2. Sun, Li & Li, Guanru & Hua, Q.S. & Jin, Yuhui, 2020. "A hybrid paradigm combining model-based and data-driven methods for fuel cell stack cooling control," Renewable Energy, Elsevier, vol. 147(P1), pages 1642-1652.
    3. Yuxiao Qin & Guodong Zhao & Qingsong Hua & Li Sun & Soumyadeep Nag, 2019. "Multiobjective Genetic Algorithm-Based Optimization of PID Controller Parameters for Fuel Cell Voltage and Fuel Utilization," Sustainability, MDPI, vol. 11(12), pages 1-20, June.
    4. Sanghyun Yun & Jinwon Yun & Jaeyoung Han, 2023. "Development of a 470-Horsepower Fuel Cell–Battery Hybrid Xcient Dynamic Model Using Simscape TM," Energies, MDPI, vol. 16(24), pages 1-22, December.
    5. Abel Rubio & Wilton Agila & Leandro González & Jonathan Aviles-Cedeno, 2023. "Distributed Intelligence in Autonomous PEM Fuel Cell Control," Energies, MDPI, vol. 16(12), pages 1-25, June.
    6. Hu, Haowen & Ou, Kai & Yuan, Wei-Wei, 2023. "Fused multi-model predictive control with adaptive compensation for proton exchange membrane fuel cell air supply system," Energy, Elsevier, vol. 284(C).
    7. Hou, Junbo & Yang, Min & Ke, Changchun & Zhang, Junliang, 2020. "Control logics and strategies for air supply in PEM fuel cell engines," Applied Energy, Elsevier, vol. 269(C).
    8. 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.
    9. Sun, Li & Li, Guanru & You, Fengqi, 2020. "Combined internal resistance and state-of-charge estimation of lithium-ion battery based on extended state observer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    10. Pei Cai & Youxue Jiang & He Wang & Liangyu Wu & Peng Cao & Yulong Zhang & Feng Yao, 2020. "Numerical Simulation on the Influence of the Longitudinal Fins on the Enhancement of a Shell-and-Tube Ice Storage Device," Sustainability, MDPI, vol. 12(6), pages 1-14, March.
    11. Chen Chen & Lei Pan & Shanjian Liu & Li Sun & Kwang Y. Lee, 2018. "A Sustainable Power Plant Control Strategy Based on Fuzzy Extended State Observer and Predictive Control," Sustainability, MDPI, vol. 10(12), pages 1-21, December.
    12. Vu, Hoang Nghia & Truong Le Tri, Dat & Nguyen, Huu Linh & Kim, Younghyeon & Yu, Sangseok, 2023. "Multifunctional bypass valve for water management and surge protection in a proton-exchange membrane fuel cell supply-air system," Energy, Elsevier, vol. 278(C).
    13. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).
    14. Qiu, Xiaoyan & Zhang, Hang & Qiu, Yiwei & Zhou, Yi & Zang, Tianlei & Zhou, Buxiang & Qi, Ruomei & Lin, Jin & Wang, Jiepeng, 2023. "Dynamic parameter estimation of the alkaline electrolysis system combining Bayesian inference and adaptive polynomial surrogate models," Applied Energy, Elsevier, vol. 348(C).
    15. Liu, Ze & Zhang, Baitao & Xu, Sichuan, 2022. "Research on air mass flow-pressure combined control and dynamic performance of fuel cell system for vehicles application," Applied Energy, Elsevier, vol. 309(C).
    16. Zhiming Zhang & Hui Ren & Song Hu & Xinfeng Zhang & Tong Zhang & Jiaming Zhou & Shangfeng Jiang & Tao Yu & Bo Deng, 2022. "Arrangement of Belleville Springs on Endplates Combined with Optimal Cross-Sectional Shape in PEMFC Stack Using Equivalent Beam Modeling and FEA," Sustainability, MDPI, vol. 14(23), pages 1-13, November.
    17. Li, Yuehua & Pei, Pucheng & Ma, Ze & Ren, Peng & Huang, Hao, 2020. "Analysis of air compression, progress of compressor and control for optimal energy efficiency in proton exchange membrane fuel cell," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    18. Zhou, Hua-Cheng & Qian, Jun-Yan & Cai, Rui-Yang, 2024. "Disturbance observer and Mittag-Leffler stabilization design for multi-dimensional fractional distributed parameter systems," Applied Mathematics and Computation, Elsevier, vol. 470(C).
    19. Lei Pan & Tong Zhang & Yuan Gao, 2023. "Real-Time Control of Gas Supply System for a PEMFC Cold-Start Based on the MADDPG Algorithm," Energies, MDPI, vol. 16(12), pages 1-20, June.
    20. Chen Chen & Jiangfan Lin & Lei Pan & Kwang Y. Lee & Li Sun, 2019. "Improving Simultaneous Cooling and Power Load-Following Capability for MGT-CCP Using Coordinated Predictive Controls," Energies, MDPI, vol. 12(6), pages 1-24, March.

    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:12:y:2019:i:2:p:231-:d:197304. 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.