Synthetic Inertia Control Strategy for Doubly Fed Induction Generator Wind Turbine Generators Using Lithium-Ion Supercapacitors

2018 ◽  
Vol 33 (2) ◽  
pp. 773-783 ◽  
Author(s):  
Jiebei Zhu ◽  
Jiabing Hu ◽  
William Hung ◽  
Chengshan Wang ◽  
Xin Zhang ◽  
...  
2014 ◽  
Vol 707 ◽  
pp. 329-332
Author(s):  
Li Ling Sun ◽  
Dan Fang

As the number of doubly fed induction generator (DFIG)- based wind-turbine systems continues to increase, wind turbines are required to provide Low Voltage Ride-Through (LVRT) capability, especially under the condition of grid voltage dips. This paper, depending on the operating characteristics of doubly-fed induction generator during grid faults ,deals with a protection and control strategy on rotor-side converter (RSC) to enhance the low voltage ride through capability of a wind turbine driven doubly fed induction generator (DFIG). The simulation and experiment studies demonstrate the correctness of the developed model and the effectiveness of the control strategy for DFIG-based wind-turbine systems under such adverse grid conditions.


2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Hammadi Laghridat ◽  
Ahmed Essadki ◽  
Maha Annoukoubi ◽  
Tamou Nasser

A novel and robust active disturbance rejection control (ADRC) strategy for variable speed wind turbine systems using a doubly fed induction generator (DFIG) is presented in this paper. The DFIG is directly connected to the main utility grid by stator, and its rotor is connected through a back-to-back three phase power converter (AC/DC/AC). Due to the acoustic nature of wind and to ensure capturing maximum energy, a control strategy to extract the available maximum power from the wind turbine by using a maximum power point tracking (MPPT) algorithm is presented. Moreover, a pitch actuator system is used to control the blades’ pitch angle of the wind turbine in order to not exceed the wind turbine rated power value in case of strong wind speeds. Furthermore, the rotor-side converter is used to control the active and reactive powers generated by the DFIG. However, the grid-side converter is used to control the currents injected into the utility grid as well as to regulate the DC-link voltage. This paper aims to study and develop two control strategies for wind turbine system control: classical control by proportional integral (PI) and the proposed linear active disturbance rejection control (LADRC). The main purpose here is to compare and evaluate the dynamical performances and sensitivity of these controllers to the DFIG parameter variation. Therefore, a series of simulations were carried out in the MATLAB/Simulink environment, and the obtained results have shown the effectiveness of the proposed strategy in terms of efficiency, rapidity, and robustness to internal and external disturbances.


Inventions ◽  
2020 ◽  
Vol 5 (3) ◽  
pp. 40
Author(s):  
Darya Khan ◽  
Jamshed Ahmed Ansari ◽  
Shahid Aziz Khan ◽  
Usama Abrar

Scientists and researchers are exploring different methods of generating and delivering electrical energy in an economical and reliable way, enabling them to generate electricity focusing on renewable energy resources. All of these possess the natural property of self-changing behavior, so the connection of these separate independent controllable units to the grid leads to uncertainties. This creates an imbalance in active power and reactive power. In order to control the active and reactive power in wind turbine generators with adjustable speed, various control strategies are used to allay voltage and current variations. This research work is focused on the design and implementation of effective control strategies for doubly fed induction generator (DFIG) to control its active and reactive power. A DFIG system with its control strategies is simulated on MATLAB software. To augment the transient stability of DFIG, the simulation results for the active and reactive power of conventional controllers are compared with three types of feed forward neural network controllers, i.e., probabilistic feedforward neural network (PFFNN), multi-layer perceptron feedforward neural network (MLPFFN) and radial basic function feedforward neural network (RBFFN) for optimum performance. Conclusive outcomes clearly manifest the superior robustness of the RBFNN controller over other controllers in terms of rise time, settling time and overshoot value.


2017 ◽  
Vol 10 (1) ◽  
pp. 56 ◽  
Author(s):  
Zakaria Sabiri ◽  
Nadia Machkour ◽  
Nabila Rabbah ◽  
Mohammed Nahid ◽  
Elm'kaddem Kheddioui

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