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A Heuristic T-S Fuzzy Model for the Pumped-Storage Generator-Motor Using Variable-Length Tree-Seed Algorithm-Based Competitive Agglomeration

Author

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  • Jianzhong Zhou

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yang Zheng

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Yanhe Xu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Han Liu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
    Hubei Key Laboratory of Digital Valley Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Diyi Chen

    (Department of Electrical Engineering, Northwest A&F University, Yangling 712100, China)

Abstract

With the fast development of artificial intelligence techniques, data-driven modeling approaches are becoming hotspots in both academic research and engineering practice. This paper proposes a novel data-driven T-S fuzzy model to precisely describe the complicated dynamic behaviors of pumped storage generator motor (PSGM). In premise fuzzy partition of the proposed T-S fuzzy model, a novel variable-length tree-seed algorithm based competitive agglomeration (VTSA-CA) algorithm is presented to determine the optimal number of clusters automatically and improve the fuzzy clustering performances. Besides, in order to promote modeling accuracy of PSGM, the input and output formats in the T-S fuzzy model are selected by an economical parameter controlled auto-regressive (CAR) model derived from a high-order transfer function of PSGM considering the distributed components in the water diversion system of the power plant. The effectiveness and superiority of the T-S fuzzy model for PSGM under different working conditions are validated by performing comparative studies with both practical data and the conventional mechanistic model.

Suggested Citation

  • Jianzhong Zhou & Yang Zheng & Yanhe Xu & Han Liu & Diyi Chen, 2018. "A Heuristic T-S Fuzzy Model for the Pumped-Storage Generator-Motor Using Variable-Length Tree-Seed Algorithm-Based Competitive Agglomeration," Energies, MDPI, vol. 11(4), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:944-:d:141298
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    References listed on IDEAS

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    1. Li, Chaoshun & Mao, Yifeng & Yang, Jiandong & Wang, Zanbin & Xu, Yanhe, 2017. "A nonlinear generalized predictive control for pumped storage unit," Renewable Energy, Elsevier, vol. 114(PB), pages 945-959.
    2. Weijia Yang & Jiandong Yang & Wencheng Guo & Wei Zeng & Chao Wang & Linn Saarinen & Per Norrlund, 2015. "A Mathematical Model and Its Application for Hydro Power Units under Different Operating Conditions," Energies, MDPI, vol. 8(9), pages 1-16, September.
    3. Zeng, Wei & Yang, Jiandong & Yang, Weijia, 2016. "Instability analysis of pumped-storage stations under no-load conditions using a parameter-varying model," Renewable Energy, Elsevier, vol. 90(C), pages 420-429.
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    Cited by:

    1. 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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