Optimal DTC Control Strategy of DFIG Using Variable Gain PI and Hysteresis Controllers Adjusted by PSO Algorithm

This paper presents dynamic modeling and control of Doubly Fed Induction Generator (DFIG) based on wind turbine systems, where the stator of DFIG is directly connected to the grid and the rotor was fed by a three level PWM NPC inverter. The active and reactive power control of the DFIG is based on the feedback technique by vector control method by using a classical regulator of Proportional-Integral (PI) type which allows us, in association with the looping of powers, to obtain an efficient and robust system. This approach is a very attractive solution for devices using DFIG as wind energy conversion systems; because, it is a simple, practical implementation, commonly applied in the wind turbine industry and it presents very acceptable performance, However, this control approach has certain limitations and has several causes, vector command with NPC three-level inverter pulse width modulation (PWM) is used to control the reactive power and active power of the generator. Then, use the neural network design to replace the traditional proportional-integral (PI) controller. Finally, the Matlab/Simulink software is used for simulation to prove the effectiveness of the command strategy.

Section: Modelling Of Doubly Fed Induction Generatormentioning

confidence: 99%

“…The working reference for the control is that related to the rotating field so that the axis (𝑑) coincides with the direction desired flow, which can be rotor, stator, or air gap. Thus, it is possible to orient the different flows of the machine [3,[17][18][19][20][21][22][23][24][25][26][27][28][29].…”

Section: Field Oriented Strategy (Foc)mentioning

confidence: 99%

Robust Neural Control of Wind Turbine Based Doubly Fed Induction Generator and NPC Three Level Inverter

Narimene

Kheira

Mohamed

2022

“…The TSR based MPPT strategy consists of keeping the speed ratio at its optimum value which corresponds to the maximum value of the power coefficient by adjusting the rotor rotational speed. The mechanical speed is expressed by [25]:…”

Section: Description Of the Wind Turbine Modelmentioning

confidence: 99%

Improved Super Twisting Based High Order Direct Power Sliding Mode Control of a Connected DFIG Variable Speed Wind Turbine

Zouheyr¹,

Baghli

Boumédiène³

2021

This work presents the theoretical and practical comparison of linear and nonlinear control laws for the direct power control of a grid-connected double fed induction generator (DFIG), based wind energy conversion system (WECS) under different operating modes. We will show the improvement brought by the super twisting based high order sliding mode control to mitigate the chattering phenomenon, due to the high switching frequency. It will also avoid the hyperlink of the controller settings to the system’s mathematical model and will reduce the sensibility to external disturbances. The overall structure of the proposed control requires the use of the DFIG simplified model with field-oriented control (FOC). This last allows an instantaneous decoupled control of the DFIG stator active and reactive power by acting on dq rotor currents (Iqr , Idr ) respectively. In the preliminary tests, a comparative study is conducted to verify the superior performance of the proposed WECS control scheme during various operating modes including the maximum power point tracking MPPT mode. The study reveals the effectiveness of each implemented control law with its advantages and drawbacks.

“…If the particle›s fitness is better than the global best fitness, then the position vector is saved for the global best. Finally, the particle's velocity and position are updated until the termination condition is satisfied [30][31][32][33]. Fig.…”

Section: The Presented Ct Saturation Detection Modelmentioning

confidence: 99%

A Hybrid Intelligent Method for Compensation of Current Transformers Saturation Based on PSO-SVR

Gorji

Hosseini

Abdoos

et al. 2021

The Current Transformers (CT) saturation may cause the protective relays mal-operation either non-recognition of internal fault or undesirable trip under external fault conditions. Therefore, compensation of CT saturation is very important for correct performance of protective schemes. Compensation of CT saturation by combination of signal processing methods and intelligent algorithms is a suitable solution to solve the problem. It decreases the probability of mal-operation and increases the reliability of the power system. In this paper, Support Vector Regression (SVR) method is employed to compensate the distorted secondary current due to CT saturation. In SVR method, despite the other methods such as MLPand ANFIS, instead of minimizing the model error, the operational risk error is considered as target function. In this method, by using Kernel tricks, a smart RBF neural network is obtained, so that all operational procedures will be optimized automatically. In this paper, an intelligent method based on Particle Swarm Optimization (PSO) algorithm is presented to determine the optimal values of SVR parameters. Due to the stability and robustness of this method in presence of noise and sudden changes in current, this method has a high accuracy. In addition, a sample power system is simulated using PSCAD software. Afterwards, current signals are extracted and fed to PSO-SVR algorithm, which is implemented in MATLAB environment. The obtained results show the preference of the proposed method in aspect of estimation accuracy as compared to some presented methods in the field of CT saturation detection and correction.