Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Elmouhi, Noureddine; Essadki, Ahmed; Elaimani, Hind

doi:10.11591/ijpeds.v13.i2.pp674-688

Cited by 7 publications

(9 citation statements)

References 25 publications

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“…It is clearly remarkable that the produced power faithfully follows its reference in the case of a DFIG with constant internal parameters. On the other hand in Figure 6, with a rotor resistance reaching 50% more than its nominal value, the power produced no longer reaches maximum values [35].…”

Section: Parameter's Optimisation Using Genetic Algorithm (Ga) For Sl...mentioning

confidence: 92%

Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm

Elaimani

Essadki²,

Elmouhi³

et al. 2022

IJEECS

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The calculation of control parameters for a system control method is based on the model of the system with assumed fixed internal parameters. However, these parameters can vary greatly due to several phenomena. This paper presents an adapted control of a doubly fed induction generator machine robust against the rotor resistance variations of the machine used as a generator in wind energy conversion systems. The adaptation is ensured by a system allowing to identify in real time the value of the resistance, the system used is mainly based on a Luenberger observer. The conversion system is divided into two parts, the first mechanical part containing the turbine and the gearbox, the second electrical one consisting of a double fed induction generator, linked on the stator side directly to the grid, and on the rotor, side linked to the grid through two power electronics converters interposed with a direct current (DC) link. The machine-side converter is used to control the active and reactive powers, and the second on the grid side is used to control the DC link voltage. The converters are controlled by the sliding mode strategy, and the validity of the methods is checked by simulation using MATLAB/Simulink.

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Section: Parameter's Optimisation Using Genetic Algorithm (Ga) For Sl...mentioning

confidence: 92%

Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm

Elaimani

Essadki²,

Elmouhi³

et al. 2022

IJEECS

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“…It is an extensive simulation of the natural evolution method of optimal solutions to some complex problems. At the core of the genetic algorithm are initial group identification, parameter encoding, genetic manipulation, fitness function, and control parameters [23]. The genetic process principally contains three factors: the selection process, the crossing process, and the variation process.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Mansoury,

El Bourakadi,

Yahyaouy

et al. 2024

Bulletin EEI

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Through the much defiance facing energy today, it has become necessary to rely on wind energy as a source of unlimited renewable energies. However, energy planning and regulation require wind capacity forecasting, because oscillations of wind speed drastically affect directly power generation. Therefore, several scenarios must be provided to allow for estimating uncertainties. To deal with this problem, this paper exploits the major advantages of the regularized extreme learning machine algorithm (R-ELM) and thus proposes a model for predicting the wind energy generated for the next hour based on the time series of wind speed. The R-ELM is combined with the genetic algorithm which is designed to optimize the most important hyperparameter which is the number of hidden neurons. Thus, the proposed model aims to forecast the average wind power per hour based on the wind speed of the previous hours. The results obtained showed that the proposed method is much better than those reported in the literature concerning the precision of the prediction and the time convergence.

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“…The MSC is responsible for controlling the production of this torque and also the level of reactive power extracted from the generator connected to the wind turbine. MPPT is performed by controlling ω r to respond to changes in v, to maintain the operation at λ opt (optimal speed ratio), according to (2). Controlling the speed of the machine will be achieved by controlling the turbine pitch angle, β.…”

Section: Generator Side Controlmentioning

confidence: 99%

“…The increase in world population has, in turn, caused greater use of energy, especially electrical energy, as an engine of the economy and as an impeller of industrial development of new technologies, and as a resource used in the daily life of people. Continuous exploitation on a large scale of natural resources is one of the big factors that has caused environmental problems to increase [1,2]. Therefore, there is a need to use renewable energies that are becoming increasingly well known, such as wind and solar energies [3,4].…”

Section: Introductionmentioning

confidence: 99%

Optimized Control for PMSG Wind Turbine Systems under Unbalanced and Distorted Grid Voltage Scenarios

Abo‐Khalil

Alobaid

2023

Sustainability

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Wind energy is one of the fastest growing energies due to its feasible cost and environment friendly nature, where there is no emission of waste or greenhouse gases emission. Wind energy systems should be connected to the electric utility to avoid the effects of the intermittent nature of these sources. One of the main problems of the utility integration of wind turbines is the unbalanced voltage of the utility grid. Many efforts have been made to remedy the effects of the unbalanced grid voltage connected with the electric utility. This research points out the need for more accurate and robust controllers, such as those mentioned in this paper. A novel combination strategy is presented in this paper to overcome the inherent imbalance in the electric utility voltages when used in combination with a permanent magnet synchronous generator (PMSG). This novel technique is designed to adapt the positive sequence of the dq-axis of the output current from the PMSG to extract the maximum power available from a wind turbine. Meanwhile, the negative sequence component is used to improve the quality of the output current from a wind turbine. A current controller is introduced in the grid side converter to compensate for the effects of the 2nd and 6th components of the output currents. Using these controllers reduces all dominant components in the output currents from wind turbines and permits a robust tracker for the maximum power available. A detailed simulation and experimental studies are introduced in this paper to show the controller operation under different operating conditions. The simulation and experimental results from the novel proposed strategy show its superiority in fully remedying the effect of unbalanced grid voltages.

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Robust control of wind turbine based on doubly-fed induction generator optimized by genetic algorithm

Cited by 7 publications

References 25 publications

Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm

Adaptive doubly fed induction generator’s control driven wind turbine using luenberger observer optimized by genetic algorithm

Optimized extreme learning machine using genetic algorithm for short-term wind power prediction

Optimized Control for PMSG Wind Turbine Systems under Unbalanced and Distorted Grid Voltage Scenarios

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