Abstract:Purpose
This paper aims to present an optimal variable speed drive of a doubly fed induction motor (DFIM) with minimum losses and reduced inverter capacity. The operation with minimum losses ensures that the DFIM develops the required load torque at desired speed with maximum energy saving. Moreover, the control of rotor voltage ensures the reduced inverter capacity. The water cycle algorithm (WCA) as one of meta-heuristic optimization techniques is used to estimate the optimal rotor voltages to drive the DFIM… Show more
“…V s , V r , are stator voltage magnitude, rotor voltage magnitude referred to stator, and rotor voltage angle, respectively. I s , I r , are stator current and referred rotor current, respectively 12 , 61 – 64 . Figure 3 Equivalent circuit per phase of DFIG steady state model.…”
The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.
“…V s , V r , are stator voltage magnitude, rotor voltage magnitude referred to stator, and rotor voltage angle, respectively. I s , I r , are stator current and referred rotor current, respectively 12 , 61 – 64 . Figure 3 Equivalent circuit per phase of DFIG steady state model.…”
The large-scale wind energy conversion system (WECS) based on a doubly fed induction generator (DFIG) has gained popularity in recent years because of its various economic and technical merits. The fast integration of WECS with existing power grids has caused negative influence on the stability and reliability of power systems. Grid voltage sags produce a high overcurrent in the DFIG rotor circuit. Such these challenges emphasise the necessity of the low voltage ride through (LVRT) capability of a DFIG for ensuring power grid stability during voltage dips. To deal with these issues simultaneously, this paper aims to obtain the optimal values of injected rotor phase voltage for DFIG and wind turbine pitch angles for all operating wind speeds in order to achieve LVRT capability. Bonobo optimizer (BO) is a new optimization algorithm that is applied to crop the optimum values of injected rotor phase voltage for DFIG and wind turbine pitch angles. These optimal values provide the maximum possible DFIG mechanical power to guarantee rotor and stator currents do not exceed the rated values and also deliver the maximum reactive power for supporting grid voltage during faults. The ideal power curve of a 2.4 MW wind turbine has been estimated to get the allowable maximum wind power for all wind speeds. To validate the results accuracy, the BO results are compared to two other optimization algorithms: particle swarm optimizer and driving training optimizer. Adaptive neuro fuzzy inference system is employed as an adaptive controller for the prediction of the values of rotor voltage and wind turbine pitch angle for any stator voltage dip and any wind speed.
“…The stator voltage vector is chosen as a reference voltage with angle zero. The stator and rotor currents equations can be considered as follow (Elkholy and Abd-Elkader, 2019; Gianto, 2021; Islam et al, 2011; Seshadri Sravan Kuma and Thukaram, 2018; Tian et al, 2014; Wu and Xie, 2018):…”
Section: Dfig Steady-state Modelmentioning
confidence: 99%
“…The stator voltage vector is chosen as a reference voltage with angle zero. The stator and rotor currents equations can be considered as follow (Elkholy and Abd-Elkader, 2019;Gianto, 2021;Islam et al, 2011;Seshadri Sravan Kuma and Thukaram, 2018;Tian et al, 2014;Wu and Xie, 2018): Where, R S is the phase resistance of stator, R r is rotor phase resistance referred to stator, L ss is the leakage inductance of stator, L sr is rotor leakage inductance referred to stator, L s is stator inductance (L s = L m + L s ), L r is rotor inductance (L r = L m + L r ), L m is magnetizing inductance, v s is stator angular frequency, v r is rotor angular frequency, S is slip, p is pole number, V s is stator voltage magnitude, V r is referred rotor voltage magnitude, and d is rotor voltage angle. The active and reactive power of stator and rotor are (Abad et al, 2011):…”
Section: Dfig Steady-state Modelmentioning
confidence: 99%
“…The most popular current MPPT methods are optimal torque method, power signal feedback method, optimal tip speed ratio method, hill climb search method, and perturb and observer method (Hemanth Kumar et al, 2018; Kumar and Chatterjee, 2016; Mousa et al, 2021). A large variety of optimization algorithms with different variants and modifications are proposed to solve many complex engineering problems, which are often non-continuous and non-linear problems (Bakir et al, 2020, 2021; Elkholy, 2018; Elkholy and Abd-Elkader, 2019; Han et al, 2021; Manohar et al, 2021; Memon et al, 2021; Shutari et al, 2021). The major benefits of these algorithms are robustness, flexibility, easy implementation, efficient, and computational efficiency compared to alternative mathematical methods (Behera et al, 2015; Sompracha et al, 2019).…”
The operation of wind power system at optimum power point is a big challenge particularly under uncertainty of wind speed. As a result, it is necessary to install an effective maximum power point tracking (MPPT) controller for extracting the available maximal power from wind energy conversion system (WECS). Therefore, this paper aims to obtain the optimal values of injected rotor phase voltage for doubly fed induction generator (DFIG) to ensure the extraction of peak power from wind turbine under different wind speeds as well as to get the optimal performance of DFIG. A new metaheuristic optimization approach; Driving Training Algorithm (DTA) is used to crop the optimal DFIG rotor voltages. Three different scenarios are presented to have MPPT, the first one is the MPPT with unity stator power factor, the second one is the MPPT with minimum DFIG losses, and the third scenario is MPPT with minimum rotor current to reduce the rating of rotor inverter. The MATLAB environment is used to simulate and study the proposed controller with 2.4 MW wind turbine. The optimum power curve of wind turbine has been estimated to get the reference values of DFIG mechanical power. The results ensured the significance and robust of the proposed controller to have MPPT under different wind speeds. The DTA results are compared with other two well-known optimization algorithms; water cycle algorithm (WCA) and particle swarm optimizer (PSO) to verify the accuracy of results.
“…According to the previous research articles, the efficiency of the DFIG‐WTs is mostly improved either by using maximum power point tracking (MPPT) schemes 4,5 or using losses minimization (LM) techniques 6,7 . It also can be improved using a combination of the MPPT schemes and LM techniques 8,9 .…”
Summary
This article deals with the improvement of maximum efficiency schemes of doubly fed induction generator–based wind turbines (DFIG‐WTs) through controlling their rotor current. The nonlinearity characteristics, parameters uncertainty, and grid disturbances in the DFIG‐WTs may affect the tracking performance and the energy conversion efficiency. The magnetizing inductance has the highest value, compared to the other DFIG parameters. Also, its value varies during grid disturbances due to main flux saturation. Therefore, improper setting of its value in the controller can result in significant performance distortion. Active disturbance rejection controller (ADRC) is a simple and robust control technique that can deal with the system's internal and external disturbances. Besides, ADRC does not strongly depend on the accurate mathematical model of the system. For further improvement of the response speed of the traditional ADRC, a novel control strategy based on a model‐assisted ADRC (MA‐ADRC), considering the variation of the magnetizing inductance, is proposed to regulate the rotor current. Furthermore, a new tuning approach is suggested to evaluate the parameters of the MA‐ADRC. Response of the presented strategy is assessed on a 1.5 MW DFIG‐WTs using MATLAB/Simulink package. The proposed MA‐ADRC is compared with the traditional proportional‐integral (PI) controller and existing ADRCs strategies under various scenarios. The simulation results validate the precise tracking performance of the suggested strategy and its robustness against grid disturbances and wind speed variations.
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