Application of multi-model control with fuzzy switching to a micro hydro-electrical power plant

被引:30
|
作者
Salhi, Issam [1 ]
Doubabi, Said [1 ]
Essounbouli, Najib [2 ]
Hamzaoui, Abdelaziz [2 ]
机构
[1] Cadi Ayyad Univ, LEST, Fac Sci & Technol Marrakesh, Gueliz, Marrakesh, Morocco
[2] Univ Reims, CReSTIC, F-10026 Troyes, France
关键词
Renewable energy; Micro hydro power plant; Takagi-Sugeno fuzzy inference system modelling; Multi-model control; NONLINEAR-SYSTEMS; DESIGN; MODELS;
D O I
10.1016/j.renene.2010.02.008
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Modelling hydraulic turbine generating systems is not an easy task because they are non-linear and uncertain where the operating points are time varying. One way to overcome this problem is to use Takagi-Sugeno (TS) models, which offer the possibility to apply some tools from linear control theory, whereas those models are composed of linear models connected by a fuzzy activation function. This paper presents an approach to model and control a micro hydro power plant considered as a non-linear system using TS fuzzy systems. A TS fuzzy system with local models is used to obtain a global model of the studied plant. Then, to combine efficiency and simplicity of design, PI controllers are synthesised for each considered operating point to be used as conclusion of an electrical load TS Fuzzy controller. The latter ensures the global stability and desired performance despite the change of operating point. The proposed approach (model and controller) is tested on a laboratory prototype, where the obtained results show their efficiency and their capability to ensure good performance despite the non-linear nature of the plant. (C) 2010 Elsevier Ltd. All rights reserved.
引用
收藏
页码:2071 / 2079
页数:9
相关论文
共 50 条
  • [41] Decentralized Indirect Adaptive Fuzzy-Neural Multi-Model Control of a Distributed Parameter Bioprocess Plant
    Baruch, Ieroham S.
    Galvan-Guerra, Rosalba
    Mariaca-Gaspar, Carlos-Roman
    Melin, Patricia
    2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8, 2008, : 1657 - +
  • [42] Model set optimization method for complex plant multi-model control
    Xu, Jiansheng
    Hou, Xiong
    Wang, Yongji
    2006 IMACS: MULTICONFERENCE ON COMPUTATIONAL ENGINEERING IN SYSTEMS APPLICATIONS, VOLS 1 AND 2, 2006, : 1880 - +
  • [44] Predictive function control based on multi-model for pH plant
    Zhang, ZH
    Wang, SQ
    NEW TECHNOLOGIES FOR COMPUTER CONTROL 2001, 2002, : 241 - 245
  • [45] Monitoring of Electrical Output Power-Based Internet of Things for Micro-Hydro Power Plant
    Ginting, Setiawan
    Simatupang, Joni Welman
    Bukhori, Iksan
    Kaburuan, Emil Robert
    2018 INTERNATIONAL CONFERENCE ON ORANGE TECHNOLOGIES (ICOT), 2018,
  • [46] Multi-model And Fuzzy PID Control for Fixed-wing UAV
    Zhou, Dali
    Geng, Qingbo
    PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MECHATRONICS, ROBOTICS AND AUTOMATION (ICMRA 2015), 2015, 15 : 523 - 528
  • [47] A fuzzy-neural multi-model for mechanical systems identification and control
    Baruch, IS
    Beltran, R
    Garrido, R
    Gortcheva, E
    2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 2113 - 2119
  • [48] Robust multi-model control of an autonomous wind power system
    Cutululis, Nicolas Antonio
    Ceanga, Emil
    Hansen, Anca Daniela
    Sorensen, Poul
    WIND ENERGY, 2006, 9 (05) : 399 - 419
  • [49] A fuzzy-neural multi-model for nonlinear systems identification and control
    Baruch, Ieroham S.
    Lopez, Rafael Beltran
    Guzman, Jose-Luis Olivares
    Flores, Jose Martin
    FUZZY SETS AND SYSTEMS, 2008, 159 (20) : 2650 - 2667
  • [50] A fuzzy-neural multi-model for mechanical systems identification and control
    Baruch, IS
    Beltran, RL
    Olivares, JL
    Garrido, R
    MICAI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2004, 2972 : 774 - 783