Robust power optimization strategy for wind-driven induction machines using type-2 and type-1 fuzzy logic controllers

This paper proposes a reliable power optimization strategy that maximizes 
the harvested power of induction machines driven by wind, taking into 
account variable wind turbulence and uncertain machine parameters. This 
work explores the challenging task of designing type-2 fuzzy logic (T2FL) 
and conventional type-1 fuzzy logic (T1FL) controllers for wind energy 
conversion systems that exhibit multiple non-linearities. T2FL controllers 
are proficient in tackling uncertainties and offer quicker and more precise 
decision-making capabilities. The proposed approach is beneficial as it is 
independent of accurate wind turbine parameters, wind speed data, or 
additional sensors. Rather, it utilizes the mechanical rotor speed and the 
wind turbine power as input, which corresponds to maximum power point 
tracking (MPPT) through the management of the rotor speed via the 
machine-side converter. Real data validates the scheme against classical 
controllers, and via a set of simulations and statistical analyses, performance 
metrics like steady-state error, overshoot, tracking speed, and efficiency are 
widely assessed. The results show that the proposed scheme, which is 
independent of a dedicated wind speed sensor, demonstrates superior 
tracking performance, lower tracking errors, such as lower RMSE/MAE, and 
higher energy yield, although the wind speed and the system parameters 
change rapidly. Overall, this design provides more robust performance to 
random wind speed variations, increases operational efficiency and wind 
turbines' service life, and is low in adding mass and cost.