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Multiobjective Optimization of a Fractional-Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA-III Algorithm under Multiworking Conditions

Author

Listed:
  • Chu Zhang
  • Tian Peng
  • Chaoshun Li
  • Wenlong Fu
  • Xin Xia
  • Xiaoming Xue

Abstract

In order to make the pump turbine governing system (PTGS) adaptable to the change of working conditions and suppress the frequency oscillation caused by the “S” characteristic area running at middle or low working water heads, the traditional single-objective optimization for fractional-order PID (FOPID) controller under single working conditions is extended to a multiobjective framework in this study. To establish the multiobjective FOPID controller optimization (MO-FOPID) problem under multiworking conditions, the integral of the time multiplied absolute error (ITAE) index of PTGS running at low and high working water heads is adopted as objective functions. An improved nondominated sorting genetic algorithm III based on Latin hypercube sampling and chaos theory (LCNSGA-III) is proposed to solve the optimization problem. The Latin hypercube sampling is adopted to generate well-distributed initial population and take full of the feasible domain while the chaos theory is introduced to enhance the global search and local exploration ability of the NSGA-III algorithm. The experimental results on eight test functions and a real-world PTGS have shown that the proposed multiobjective framework can improve the Pumped storage units’ adaptability to changeable working conditions and the proposed LCNSGA-III algorithm is able to solve the MO-FOPID problem effectively.

Suggested Citation

  • Chu Zhang & Tian Peng & Chaoshun Li & Wenlong Fu & Xin Xia & Xiaoming Xue, 2019. "Multiobjective Optimization of a Fractional-Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA-III Algorithm under Multiworking Conditions," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    • Handle: RePEc:hin:complx:5826873
    DOI: 10.1155/2019/5826873
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    References listed on IDEAS

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    8. Chu Zhang & Chaoshun Li & Tian Peng & Xin Xia & Xiaoming Xue & Wenlong Fu & Jianzhong Zhou, 2018. "Modeling and Synchronous Optimization of Pump Turbine Governing System Using Sparse Robust Least Squares Support Vector Machine and Hybrid Backtracking Search Algorithm," Energies, MDPI, vol. 11(11), pages 1-21, November.
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    Cited by:

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    2. Wenlong Fu & Jiawen Tan & Xiaoyuan Zhang & Tie Chen & Kai Wang, 2019. "Blind Parameter Identification of MAR Model and Mutation Hybrid GWO-SCA Optimized SVM for Fault Diagnosis of Rotating Machinery," Complexity, Hindawi, vol. 2019, pages 1-17, April.
    3. Zou, Yidong & Hu, Wenqing & Xiao, Zhihuai & Wang, Yunhe & Chen, Jinbao & Zheng, Yang & Qian, Jing & Zeng, Yun, 2023. "Design of intelligent nonlinear robust controller for hydro-turbine governing system based on state-dynamic-measurement hybrid feedback linearization method," Renewable Energy, Elsevier, vol. 204(C), pages 635-651.
    4. Dong, Wenhui & Cao, Zezhou & Zhao, Pengchong & Yang, Zhenbiao & Yuan, Yichen & Zhao, Ziwen & Chen, Diyi & Wu, Yajun & Xu, Beibei & Venkateshkumar, M., 2023. "A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system," Renewable Energy, Elsevier, vol. 207(C), pages 1-12.
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    6. Yonggang Li & Jinjiao Hou & Juan Gu & Chaoshun Li & Yanhe Xu, 2022. "Dynamic Characteristics and Successive Start-Up Control Strategy Optimization of Pumped Storage Units under Low-Head Extreme Conditions," Energies, MDPI, vol. 15(15), pages 1-19, July.

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