Pitch Control of Wind Turbine Blades Using Fractional Particle Swarm Optimization

To achieve the maximum power from wind in variable-speed regions of wind turbines (WTs), a suitable control signal should be applied to the pitch angle of the blades. However, the available uncertainty in the modeling of WTs complicates calculations of these signals. To cope with this problem, an optimal controller is suitable, such as particle swarm optimization (PSO). To improve the performance of the controller, fractional order PSO (FPSO) is proposed and implemented. In order to construct this approach for a two-mass WT, we propose a new state feedback, which was first applied to the turbine. The idea behind this state feedback was based on the Taylor series. Then, a linear model with uncertainty was obtained with a new input control signal. Thereafter, the conventional PSO (CPSO) and FPSO were used as optimal controllers for the resulting linear model. Finally, a comparison was performed between CPSO and FPSO and the fuzzy Takagi-Sugeno-Kang (TSK) inference system. The provided comparison demonstrates the advantages of the Taylor series with combination to these controllers. Notably, without the state feedback, CPSO, FPSO, and TSK fuzzy systems cannot stabilize WTs in tracking the desired trajectory.

Automated summary

In order to maximize power from wind turbines in variable-speed regions, a suitable control signal needs to be applied to the pitch angle of the blades. However, uncertainties in turbine modeling complicate the calculation of these signals. To address this issue, an optimal controller using particle swarm optimization (PSO) is proposed, with further improvement through fractional order PSO (FPSO). By utilizing a new state feedback based on Taylor series, a linear model with uncertainty is obtained and used for the implementations of CPSO, FPSO, and fuzzy Takagi-Sugeno-Kang (TSK) inference system. Comparison of the controllers demonstrates the advantages of the Taylor series in stabilizing wind turbines and tracking desired trajectories.