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A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant

Author

Listed:
  • Yuxiao Qin

    (Key Lab of Energy Thermal Conversion and Control of Ministry of Education, Chien-Shiung Wu College, Southeast University, Nanjing 210096, China)

  • Li Sun

    (Key Lab of Energy Thermal Conversion and Control of Ministry of Education, Chien-Shiung Wu College, Southeast University, Nanjing 210096, China)

  • Qingsong Hua

    (School of Mechanical and Electrical Engineering, Qingdao University, Ningxia Road 308, Qingdao 266071, China)

  • Ping Liu

    (School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China)

Abstract

Solid oxide fuel cells (SOFCs) are promising electrochemical devices which translate chemical energy directly into electric energy with high efficiency and low pollution. However, the control of the output voltage of SOFCs is quite challenging because of the strong nonlinearity, limited fuel flow, and rapid variation of the load disturbance. Nowadays, proportional-integral-derivative (PID) controllers are commonly utilized in industrial control systems for their high reliability and simplicity. However, it will lead to overshoot and windup issues when used in the wide-range operation of SOFCs. This paper aims to improve the PID controller performance based on fuzzy logic by (1) identifying a linear model based on the least squares method; (2) optimizing the PID parameters based on the generated linear model; and (3) designing a fuzzy adaptive PID controller based on the optimized parameters. The simulation results of the conventional PID controller and the fuzzy adaptive PID controller are compared, demonstrating that the proposed controller can achieve satisfactory control performance for SOFCs in terms of anti-windup, overshoot reduction, and tracking acceleration. The main contribution of this paper can be summarized as: (1) this paper identifies the SOFC model and uses the identified model as a control object to optimize conventional PID controllers; (2) this paper combines a fuzzy logic control scheme and PID control scheme to design our proposed fuzzy adaptive PID controller; and (3) this paper develops an anti-windup structure based on a back-calculation method to reduce saturation time and overshoot.

Suggested Citation

  • Yuxiao Qin & Li Sun & Qingsong Hua & Ping Liu, 2018. "A Fuzzy Adaptive PID Controller Design for Fuel Cell Power Plant," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:7:p:2438-:d:157601
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    References listed on IDEAS

    as
    1. Li Sun & Qingsong Hua & Jiong Shen & Yali Xue & Donghai Li & Kwang Y. Lee, 2017. "A Combined Voltage Control Strategy for Fuel Cell," Sustainability, MDPI, vol. 9(9), pages 1-15, August.
    2. Das, Vipin & Padmanaban, Sanjeevikumar & Venkitusamy, Karthikeyan & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Siano, Pierluigi, 2017. "Recent advances and challenges of fuel cell based power system architectures and control – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 10-18.
    3. Takaya Ogawa & Mizutomo Takeuchi & Yuya Kajikawa, 2018. "Comprehensive Analysis of Trends and Emerging Technologies in All Types of Fuel Cells Based on a Computational Method," Sustainability, MDPI, vol. 10(2), pages 1-30, February.
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    Cited by:

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    2. Yuxiao Qin & Li Sun & Qingsong Hua, 2018. "Environmental Health Oriented Optimal Temperature Control for Refrigeration Systems Based on a Fruit Fly Intelligent Algorithm," IJERPH, MDPI, vol. 15(12), pages 1-15, December.
    3. Jie Ma & Suning Ma & Xinyi Zhang & Daifen Chen & Juan He, 2018. "Development of Large-Scale and Quasi Multi-Physics Model for Whole Structure of the Typical Solid Oxide Fuel Cell Stacks," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    4. Jong-Chan Kim & Jun-Ho Huh & Jae-Sub Ko, 2019. "Improvement of MPPT Control Performance Using Fuzzy Control and VGPI in the PV System for Micro Grid," Sustainability, MDPI, vol. 11(21), pages 1-27, October.
    5. 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.

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