Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Abstract

Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, 
 a suggestion of using a compination of each controller was introduced.

A review of swarm-based metaheuristic optimization techniques and their application to doubly fed induction generator

“…CSA proved to be an unsuitable method for PI controller tuning for standard control Bat algorithm Fast convergence rate [118] Poor accuracy Easily trapped in local minima [114] Optimization of parameters of sliding mode controller [123] BA optimized sliding mode controller was superior to the conventional sliding mode controller and PI controller tuning with respect to rise time and settling time. However, there did exist a minor unbalance in the stator voltage waveforms Squirrel search algorithm Strong stability [124] Poor accuracy Easily trapped in local minima [124] Not yet established Not yet established Moth flame optimization Robust selection capability [131] Easily entrapped in local optima Stagnant convergence rate [128] , [131] , [132] Optimization of PI controller gains for standard control [133] When compared to various other MOT, MFO optimized PI controllers displayed enhanced maximum power point and fault ride through capabilities Sailfish optimization algorithm Fast convergence rate Not easily trapped in local minima [134] Not yet established Not yet established Not yet established Cuckoo search algorithm Requires knowledge of only a few parameters [141] Slow convergence rate [142] Easily trapped in local minima [141] , [142] Optimization of PI controller gains for standard control Optimization of FOPID controller gains for standard control [143] Regarding both the PI and FOPID controllers optimized using CuSA, very little analysis was provided. Critical aspects such as rise time and settling time were not considered.…”

“…CuSA was applied to both a PI controller and FOPID controller to control a DFIG in [143] . The method applied this control to the pitch controller, RSC and GSC.…”

Switched capacitor based multi-level boost inverter for smart grid applications

“…Multilevel inverters with novel switched capacitor-based designs have been proposed by various authors [27]- [29]. These designs focus on reliability while reducing the costs of the components and power losses.…”

Hybrid control mechanism-based DVR for mitigation of voltage sag and swell in solar PV-based IEEE 33 bus system

“…The symmetrical and asymmetrical voltage sags conditions are analyzed and compensated using CAD software [29]. Gabalawy et al [30] presented the double feed induction generator (DFIG) based wind turbines using the cuckoo search algorithm (CSA) to improve the output power. The PI controller and fractional order proportional-integral-derivative (FOPID) based control mechanism is introduced in CSA as an optimization approach to enhance the controller output response.…”