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Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network

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  • Hou, Guolian
  • Xiong, Jian
  • Zhou, Guiping
  • Gong, Linjuan
  • Huang, Congzhi
  • Wang, Shunjiang

Abstract

The coordinated control systems (CCS) in ultra-supercritical thermal power unit, like many other industrial systems, is a complex multivariable system with severe nonlinearity, strong multivariable coupling and uncertainties. In order to meet the requirements of operational stability, economy. etc in ultra-supercritical unit, it is necessary to establish its accurate mathematical model and further design the advanced controller. Against this background, a new fuzzy neural network modeling method is proposed in this paper. First of all, the incremental model is considered separately to improve the rationality of the local linear model structure. Then, the parameters in antecedent part is initialized by a kernel k-means++ algorithm, in which Xie-Beni index is used to optimize the number of fuzzy rules. Finally, supervised adaptive gradient descent algorithm and artificial immune particle swarm optimization algorithm work in stages to complete the training of the consequent part parameters. The proposed modeling method in this paper is applied to a 1000 MW unit in China and shows satisfactory accuracy. In the established model, the MSE of power output, main steam pressure and separator outlet steam temperature are 0.0099, 1.21E-4, 0.0023, respectively. Both numerical and graphical simulation results confirm the effectiveness of the presented fuzzy neural network in modeling.

Suggested Citation

  • Hou, Guolian & Xiong, Jian & Zhou, Guiping & Gong, Linjuan & Huang, Congzhi & Wang, Shunjiang, 2021. "Coordinated control system modeling of ultra-supercritical unit based on a new fuzzy neural network," Energy, Elsevier, vol. 234(C).
    • Handle: RePEc:eee:energy:v:234:y:2021:i:c:s0360544221014791
    DOI: 10.1016/j.energy.2021.121231
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    2. Hou, Guolian & Huang, Ting & Zheng, Fumeng & Gong, Linjuan & Huang, Congzhi & Zhang, Jianhua, 2023. "Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy," Energy, Elsevier, vol. 263(PB).
    3. Zhu, Hengyi & Tan, Peng & He, Ziqian & Zhang, Cheng & Fang, Qingyan & Chen, Gang, 2022. "Nonlinear model predictive control of USC boiler-turbine power units in flexible operations via input convex neural network," Energy, Elsevier, vol. 255(C).
    4. Hou, Guolian & Huang, Ting & Huang, Congzhi, 2023. "Flexibility improvement of 1000 MW ultra-supercritical unit under full operating conditions by error-based ADRC and fast pigeon-inspired optimizer," Energy, Elsevier, vol. 270(C).
    5. 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).
    6. Dong, Zhe & Li, Bowen & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2022. "Power-pressure coordinated control of modular high temperature gas-cooled reactors," Energy, Elsevier, vol. 252(C).
    7. Esmaeili, Mohammad & Moradi, Hamed, 2023. "Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem," Energy, Elsevier, vol. 282(C).
    8. Opriș, Ioana & Cenușă, Victor-Eduard, 2023. "Parametric and heuristic optimization of multiple schemes with double-reheat ultra-supercritical steam power plants," Energy, Elsevier, vol. 266(C).
    9. Hou, Guolian & Gong, Linjuan & Hu, Bo & Su, Huilin & Huang, Ting & Huang, Congzhi & Fan, Wei & Zhao, Yuanzhu, 2022. "Application of fast adaptive moth-flame optimization in flexible operation modeling for supercritical unit," Energy, Elsevier, vol. 239(PA).
    10. Huang, Congzhi & Li, Zhuoyong, 2023. "Data-driven modeling of ultra-supercritical unit coordinated control system by improved transformer network," Energy, Elsevier, vol. 266(C).

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