الفهرس 
Control of DFIG Wind Turbine Generator SystemOnly 14 pages are availabe for public view
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Abstract
Distributed generation (DG) systems based on renewable energy resources play an important role in electric power systems. The estimated share of these resources in electrical networks will increase significantly in the near future by providing different benefits like cost reduction, reliability of main grid, and emission reduction.
In this thesis wind energy DG system that uses Doubly Fed Induction Generators (DFIGs) is considered. The DFIG is a special type of induction machine which is comprised of two back-to-back converters. One converter connects the DFIG stator to the grid, and the second converter is connected to the rotor of the machine through a DC-link capacitor.
In this work, DFIG model has been created in the DQO reference frame. The wind turbine (WT) system is modelled by considering typical wind turbine aerodynamic characteristics under variable wind and pitch angle conditions. The variable speed WT system equipped with DFIG is normally controlled by a set of controllers. Tuning these controllers is a tedious work and it is difficult to tune the gains of controllers optimally due to the nonlinearity and the high complexity of the system.
Design an optimal control strategy is the most important part to integrate wind energy system as a renewable DG resource into the electricity grid. Today this has become an important challenge for the utilization and control of electric power systems, because of the fluctuating and intermittent behaviour of wind power generation.
The work presented in this thesis includes analysis, modelling, and optimal control system design for a DFIG based wind farm integrated to distribution network.
Among many enabling technologies a new method using particle swarm optimization (PSO) is proposed for tuning the different controllers’ parameters of a DFIG based WT system. The PSO algorithm is employed in the proposed parameter tuning method to search for the parameters of controllers to achieve the optimal adapted control of WT system. A new time-domain fitness function is illustrated with the PSO-based optimization algorithm to optimize the controllers’ parameters and measure the performance of the controllers. Briefly, PSO is used to adjust the proposed controllers of the DFIG wind turbine system. The results of the PSO algorithm on the system performance are summarized and compared to Genetic Algorithm (GA).
The generic dynamic model of WT with DFIG and its associated controllers (pitch controller, rotor side converter controller, and grid side converter controller) are presented. Proportional Integral (PI) controllers are used and compared to Proportional Integral Derivative (PID) and Fuzzy controllers. With the PSO optimized controllers’ parameters, the system stability is improved under large and small disturbances. Furthermore, the dynamic performance of the WT with DFIG can be improved.
Simulation results show that the proposed design approach is efficient to find the optimal parameters of the controllers and therefore improves the transient performance of the WTGS over a wide range of operating conditions. The simulation studies are carried out on a 9MW DFIG based wind farm connected to 25kV distribution system exports power to a 120 kV grid and the results show that the integrated power generation can supply steady output power at different case studies.
The superiority of the used algorithm results has been established and compared. Simulation results and models have been developed using Matlab/Simulink.