Variable speed wind turbine based on doubly fed induction generator using genetic algorithms

Optimization of the control of doubly fed induction generators (DFIGs) is essential for many applications, such as renewable energy systems, industrial automation, and electric cars. Unfortunately, the dynamic and non-linear character of DFIG frequently makes it difficult for conventional control techniques to adjust, which results in less-than-ideal performance. To overcome these obstacles, this paper presents an optimized fuzzy speed control of a doubly fed induction wind generator using a genetic algorithm, which has more advantages than its counterpart PI speed controller. In this study, the modeling of generator in the Park's frame was presented, as well as its indirect vector control applied to the stator flux. Then, to guarantee tracking of the ideal operating point in real-time and to produce the most electricity possible for varying wind speeds, we used a fuzzy PI speed controller. To improve the sizing operation of this controller, we opted for the genetic algorithm technique combined with one of the local search methods, which facilitated the search and reduced the effort compared to the trial-and-error sizing method. Furthermore, this made it possible for the wind system to track the optimal power point maximum with good performance. The simulation results of the suggested control displayed by MATLAB-Simulink illustrate the effectiveness and adaptability of the proposed control scheme across different operating conditions. The analysis of the results showed good performance for speed, small voltage and current ripple when using the fuzzy PI speed controller with genetic algorithm technique. offering promising prospects for practical implementation in variable speed wind turbine applications.