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Gain scheduling control of waste heat energy conversion systems based on an LPV (linear parameter varying) model

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  • Zhang, Jianhua
  • Lin, Mingming
  • Fang, Fang
  • Xu, Jinliang
  • Li, Kang

Abstract

This paper investigates gain scheduling control strategy for ORC (organic Rankine cycle) based WHECSs (waste heat energy conversion systems) over a wide range of operating conditions. A low order physical model is first developed for ORC based WHECSs. Then the nonlinear dynamics of the WHECS are formulated by an affine LPV (linear parameter varying) system. The generated offline LPV system is characterized by dependence on the mass flow rate and temperature of the waste heat at the inlet of the evaporator. The gain scheduling controller based on the LPV model ensures that the WHECS can obtain satisfactory performance over a wide range operating region. The simulation results demonstrate the effectiveness of the proposed controller.

Suggested Citation

  • Zhang, Jianhua & Lin, Mingming & Fang, Fang & Xu, Jinliang & Li, Kang, 2016. "Gain scheduling control of waste heat energy conversion systems based on an LPV (linear parameter varying) model," Energy, Elsevier, vol. 107(C), pages 773-783.
    • Handle: RePEc:eee:energy:v:107:y:2016:i:c:p:773-783
    DOI: 10.1016/j.energy.2016.04.064
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    Cited by:

    1. Wu, Xialai & Chen, Junghui & Xie, Lei, 2018. "Integrated operation design and control of Organic Rankine Cycle systems with disturbances," Energy, Elsevier, vol. 163(C), pages 115-129.
    2. Shi, Yao & Zhang, Zhiming & Chen, Xiaoqiang & Xie, Lei & Liu, Xueqin & Su, Hongye, 2023. "Data-Driven model identification and efficient MPC via quasi-linear parameter varying representation for ORC waste heat recovery system," Energy, Elsevier, vol. 271(C).
    3. Ying Zhang & Li Zhao & Shuai Deng & Ming Li & Yali Liu & Qiongfen Yu & Mengxing Li, 2022. "Novel Off-Design Operation Maps Showing Functionality Limitations of Organic Rankine Cycle Validated by Experiments," Energies, MDPI, vol. 15(21), pages 1-19, November.
    4. Lin, Runze & Luo, Yangyang & Wu, Xialai & Chen, Junghui & Huang, Biao & Su, Hongye & Xie, Lei, 2024. "Surrogate empowered Sim2Real transfer of deep reinforcement learning for ORC superheat control," Applied Energy, Elsevier, vol. 356(C).
    5. Imran, Muhammad & Pili, Roberto & Usman, Muhammad & Haglind, Fredrik, 2020. "Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges," Applied Energy, Elsevier, vol. 276(C).
    6. Zhang, Ying & Deng, Shuai & Ni, Jiaxin & Zhao, Li & Yang, Xingyang & Li, Minxia, 2017. "A literature research on feasible application of mixed working fluid in flexible distributed energy system," Energy, Elsevier, vol. 137(C), pages 377-390.
    7. Hernandez, Andres & Desideri, Adriano & Gusev, Sergei & Ionescu, Clara M. & Den Broek, Martijn Van & Quoilin, Sylvain & Lemort, Vincent & De Keyser, Robin, 2017. "Design and experimental validation of an adaptive control law to maximize the power generation of a small-scale waste heat recovery system," Applied Energy, Elsevier, vol. 203(C), pages 549-559.
    8. Wu, Xialai & Chen, Junghui & Xie, Lei, 2019. "Fast economic nonlinear model predictive control strategy of Organic Rankine Cycle for waste heat recovery: Simulation-based studies," Energy, Elsevier, vol. 180(C), pages 520-534.

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