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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
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    References listed on IDEAS

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    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.
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    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.

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