1
Department of Electrical Engineering, University of Kashan, Kashan, Iran
2
Shiraz University of Technology
Abstract
Power quality has become a serious problem as wind power has grown more integrated into the power grid. Power quality includes issues like flicker and harmonics. Wind characteristics such as wind speed, tower shadow and wind shear effects, and grid conditions such as short circuit capacity ratio and grid impedance angle, and maximum power point tracking type are just some of the factors that influence flicker emission and power oscillations of grid-connected wind turbines. Doubly fed induction generators (DFIG)s have several advantages over other wind power generators such as low equipment price and flexibility. However, DFIG is very sensitive to the lack of voltage at the generator terminal. Because the voltage drop induces large excitation voltage in the rotor windings, and high transient current through the rotor may occur which increase the dc interface voltage in the electronic power converter, resulting in damage to the electronic power converter. In this paper, performance of two maximum power point tracking techniques optimal torque control (OTC) and tip speed ratio (TSR) control in fluctuations emission is examined after the ideal MPPT technique and grid conditions are identified, and fluctuations caused by the tower shadow are then reduced by adding filter. The aerodynamic, mechanical, and electrical aspects of a grid-connected wind farm are simulated using MATLAB/SIMULINK. The simulation results are shown, which clearly illustrate that the proposed technique is effective for decreasing power fluctuations.
MansouriKiaei, Z., & Rajaei, A. (2026). Reducing Active Power Deviations Caused by Tower Shadowing DFIG Wind Turbine Using Band Reject Filter. (e741508). Sustainable Energy and Artificial Intelligence, (), e741508
MLA
MansouriKiaei, Z., & Rajaei, A. "Reducing Active Power Deviations Caused by Tower Shadowing DFIG Wind Turbine Using Band Reject Filter" .e741508 , Sustainable Energy and Artificial Intelligence, , 2026, e741508.
HARVARD
MansouriKiaei Z., Rajaei A. (2026). 'Reducing Active Power Deviations Caused by Tower Shadowing DFIG Wind Turbine Using Band Reject Filter', Sustainable Energy and Artificial Intelligence, (), e741508.
CHICAGO
Z. MansouriKiaei & A. Rajaei, "Reducing Active Power Deviations Caused by Tower Shadowing DFIG Wind Turbine Using Band Reject Filter," Sustainable Energy and Artificial Intelligence, (2026): e741508,
VANCOUVER
MansouriKiaei Z., Rajaei A. Reducing Active Power Deviations Caused by Tower Shadowing DFIG Wind Turbine Using Band Reject Filter. SEAI. 2026;():e741508.