Improved control for DFIG based wind power system under voltage dips using ADRC optimized by genetic algorithms

Abstract

Many countries have focused on the study of the electrical energy production using wind generators. These studies include maintaining the production under disturbed conditions at the grid connection point of wind farms, and maintaining production during voltage dips. Electrical grid operators have established different requirements for connecting renewable energy production systems to electrical grids. In this research paper, we are interested in the study of the wind power system based on the Doubly Fed Induction Generator during a voltage dip. We are also developing a control approach based on Active Disturbance Rejection Control and Genetic Algorithms, which aims to control the stator flux variations which should be taken into account during the current controllers computing. This control strategy will allow the wind power system to remain connected to the grid under voltage dips, and to resume the normal operation after the fault disappearance. The model of the wind power system and the proposed control strategy, are tested in the MATLAB-Simulink software.

A Comparative Experimental Investigation of MPPT Controls for Variable Speed Wind Turbines

“…The reference of the electromagnetic torque T em_ref allows making the mechanical generator speed equal to the reference speed Ω ref by the relation below, [58,59] :…”

Active Disturbance Rejection Control of Five-Phase Motor Based on Parameter Setting of Genetic Algorithm

“…Therefore, the steps of deriving and simplifying complex system transfer functions are omitted, which saves the time required to adjust controller parameters and has great universality. In recent years, many intelligent optimization algorithms have been applied to controller parameter settings, including genetic algorithms, the ant colony algorithm, chaotic whale algorithm, simulated annealing algorithm, immune algorithm, and differential evolution algorithm [12][13][14][15][16][17][18][19][20][21].…”

“…It has been previously used for setting parameters of active disturbance rejection controllers in motors. In order to realize the maximum power tracking of a photovoltaic generating set, N Elmouhi et al [14] used an ADRC to control a PWM rectifier on the output side of doubly-fed induction generator. In order to achieve better tracking performance, a genetic algorithm was used to adjust ADRC parameters.…”

“…Therefore, this paper proposes using a genetic algorithm to tune the parameters of a five-phase-motor ADRC. Compared with other optimization algorithms in [14,15], the genetic algorithm proposed in this paper has the characteristics of clear tuning objectives and accurate tuning parameters, and the adjusted ADRC has strong robustness. The algorithm proposed in this article not only saves time in parameter tuning but also obtains the optimal control parameters, which has great application scenarios and practical value.…”