Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Ahmed M Shawqran; Abdallah El-Marhomy; Mahmoud A Attia; Almoataz Y Abdelaziz; Hassan Haes Alhelou

doi:10.1016/j.heliyon.2021.e08239

Enhancement of wind energy conversion system performance using adaptive fractional order PI blade angle controller

Heliyon. 2021 Oct 22;7(10):e08239. doi: 10.1016/j.heliyon.2021.e08239. eCollection 2021 Oct.

Authors

Ahmed M Shawqran¹, Abdallah El-Marhomy¹, Mahmoud A Attia², Almoataz Y Abdelaziz³, Hassan Haes Alhelou⁴

Affiliations

¹ Physics and Engineering Mathematics, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
² Electrical Power and Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
³ Faculty of Engineering and Technology, Future University in Egypt, Cairo, Egypt.
⁴ School of Electrical and Electronic Engineering, University College Dublin, Ireland.

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 ( $K_{p}, K_{i}$ ) 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.

Keywords: Adaptive PI; Adaptive fractional order PI; Blade angle controller; Equilibrium optimization EO; Fractional calculus; Wind energy.