Abstract

Wind energy is considered as one of the rapidest rising renewable energy systems. Thus, in this paper the wind energy performance is enhanced through using a new adaptive fractional order PI (AFOPI) blade angle controller. The AFOPI controller is based on the fractional calculus that assigns both the integrator order and the fractional gain. The initialization of the controller parameters and the integrator order are optimized using the Harmony search algorithm (HSA) hybrid Equilibrium optimization algorithm (EO). Then, the controller gains (Kp,Ki) are auto-tuned. The validation of the new proposed controller is carried out through comparison with the traditional PID and the Adaptive PI controllers under normal and fault conditions. The fractional adaptive PI improved the wind turbine's electrical and mechanical behaviors. The adaptive fractional order PI controller has been subjected to other high variation wind speed profiles to prove its robustness. The controller showed robustness to the variations in wind speed profile and the nonlinearity of the system. Also, the proposed controller (AFOPI) assured continuous wind power generation under these sharp variations. Moreover, the active power statistical analysis of the AFOPI showed increase in energy captured of around 25 %, and reduction in the standard deviation and root mean square error of around 10% compared to the other controllers.

Highlights

  • The increased population census forced the world to increase investments in improving sustainable energy sources

  • Wind is a plentiful source of clean energy. It is one of the rapidest-rising renewable energy technologies according to the Global Wind Energy Council (GWEC)

  • The controller acquires the advantage of both: adaptive PI and classical fractional order PI

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Summary

Introduction

The increased population census forced the world to increase investments in improving sustainable energy sources. While in the third region, the blade angle controller is operated to keep the power at its rated value under gusty wind speeds. The wind turbine pitch angles are controlled by several techniques such as the proportional-integral-derivative (PID) and PI [6] They are widely applied due to its advantages. The wind turbine blade angle is controlled by new techniques such as: adaptive controllers, fuzzy logic, and sliding mode. While fuzzy logic is considered as a supreme method to control wind turbine blade angles due to the fluctuations in the wind speed profiles, which affect many factors such as: wind system dynamics, mechanical torque, and the speed of the generator rotor. Different wind speed profiles are applied to the new proposed adaptive controller to assure its robustness.

Wind turbine modelling equations
Blade pitch angle controller
Techniques of optimization algorithms
Genetic algorithm GA
Teaching learning based optimization TLBO
Equilibrium optimization EO
Validation under normal conditions
Validation under faulty conditions
First case study
Second case study
Contribution
Findings
Conclusion

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