Modelling Of Doubly-Fed Induction Generator With Variable Wind Speed

The integration of renewable energy sources, particularly wind power, into the electrical grid has gained significant attention in current years. The Doubly Fed Induction Generator (DFIG) is a mostly used generator in modern wind turbine systems due to its advantages such as improved efficiency, variable speed operation, and enhanced grid stability. However, the performance of a DFIG is highly influenced by the variable wind speed conditions it operates under. This paper presents a comprehensive modelling approach for a DFIG system considering variable wind speeds. The proposed model incorporates the dynamic characteristics of the wind turbine and DFIG components, such as the mechanical system, electrical circuits, and control strategies. The wind speed profile is taken into account, enabling the analysis of the DFIG system's behaviors under different operating conditions. The model is developed using mathematical equations and simulation techniques to accurately represent the interactions between the wind turbine, DFIG, and the grid. Various control strategies, including the rotor-side and grid-side converters, are implemented to optimize the power extraction, enhance grid stability, and mitigate potential issues such as grid faults and voltage variations. The MATLAB simulated results show the effectiveness of this proposed model is captured by the dynamic behaviors of the DFIG system with variable wind speeds. The model provides valuable insights into the system's behaviors, enabling the evaluation of performance metrics such as power output, rotor speed, and control responses. Moreover, the model can be utilized for analyzing the impact of different wind speed profiles, investigating the effectiveness of control strategies, and optimizing the design and operation of DFIG-based wind turbine systems. Overall, the developed model offers a reliable tool for understanding and analyzing the performance of DFIG systems under variable wind speed conditions. It can aid in the development of advanced control algorithms, grid integration studies, and decision-making processes for wind power generation projects.