Pitch angle control of squirrel‐cage induction generator‐based wind farm using fractional order PID and neural network controllers

Abstract Wind farms constantly inject varying power into the traditional power grid, affecting its operation and resulting in power quality and stability issues. Multivariable‐based control strategies for pitch angle control are most widely utilized to stabilize the fluctuations in the output wind power. In this research work, Fractional Order PID (FOPID) and Model Reference Neural Network (MR‐NN) controllers are proposed for controlling the pitch angle of a squirrel‐cage induction generator (SCIG)‐based wind turbine to stabilize the output active power under varying wind speeds. For comparison purposes, an Integer Order PI (IOPI) controller is also designed and implemented. Particle Swarm Optimization, Artificial Bee Colony (ABC), and Genetic Algorithm have been used to optimize the tuning parameters of both the IOPI and FOPID controllers. All the optimizing algorithms and controllers have been designed and realized in MATLAB. A comparative analysis of all the optimized variants of IOPI and FOPI controllers with MR‐NN controllers has been performed. The FOPID controller using the ABC algorithm outperforms all other control techniques in both steady‐state and transient‐state performance. The FOPID‐ABC controller achieved the fastest settling time (27.3 s) and the lowest steady‐state error (−0.0006), demonstrating superior stability and precision in power regulation.