Intelligent Control of an Experimental Small-Scale Wind Turbine

Monica Borunda (), Raul Garduno, Javier de la Cruz Soto and Rafael Alfonso Figueroa Díaz
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Monica Borunda: CONAHCYT—Tecnológico Nacional de México—Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca 62490, Morelos, Mexico
Raul Garduno: Instituto Nacional de Electricidad y Energías Limpias, Cuernavaca 62490, Morelos, Mexico
Javier de la Cruz Soto: Sonora Institute of Technology—Electric and Electronic Department, Obregon 85000, Sonora, Mexico
Rafael Alfonso Figueroa Díaz: Sonora Institute of Technology—Electric and Electronic Department, Obregon 85000, Sonora, Mexico

Energies, 2024, vol. 17, issue 22, 1-31

Abstract: Nowadays, wind turbines are one of the most popular devices for producing clean and renewable electric energy. The rotor blades catch the wind’s kinetic energy to produce rotational energy from the turbine and electric energy from the generator. In small-scale wind turbines, there are several methods to operate the blades to obtain the desired speed of rotation and power outputs. These methods include passive stall, active stall, and pitch control. Pitch control sets the angular position of the blades to face the wind to achieve a predefined relationship between turbine speed or power and wind velocity. Typically, conventional Proportional Integral (PI) controllers are used to set the angular position of the rotor blades or pitch angle. Nevertheless, the quality of speed or power regulation may vary substantially. This study introduces a rotor speed controller for a pitch-controlled small-scale wind turbine prototype based on fuzzy logic concepts. The basics of fuzzy systems required to implement this kind of controller are presented in detail to counteract the lack of such material in the technical literature. The knowledge base of the fuzzy speed controller is composed of Takagi–Sugeno–Kang (TSK) fuzzy inference rules that implement a dedicated PI controller for any desired interval of wind velocities. Each wind velocity interval is defined with a fuzzy set. Simulation experiments show that the TSK fuzzy PI speed controller can outperform the conventional PI controller in the speed and accuracy of response, stability, and robustness over the whole range of operation of the wind turbine prototype.

Keywords: fuzzy systems; fuzzy PI control; wind turbine; rotor speed; feasibility demonstration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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