Fractional Order Controller Design for Wind Turbines

According to recent studies, it has been concluded that renewable electricity generation is being requested to replace all other fuels more often. In China and the USA, among renewable electricity sources, wind usage has increased significantly compared to 2020. Given these circumstances, the aim of this study was to develop a suitable speed control method for wind power systems in order to achieve maximum power generation while reducing mechanical loads. Several control strategies have been proposed in the literature, all of which offer a compromise between performance and robustness. The present research developed fractional order PID (FOPID) controllers and proved which would be the most suitable controller to address the challenges that wind turbine systems face. The parameters of the FOPID controllers (K-P, K-I, K-D, lambda and mu) were tuned with the help of the following optimization algorithms: a genetic algorithm (GA), a multi-objective genetic algorithm (MOGA) and particle swarm optimization (PSO). The results from these three turning methods were then compared to find the method that offered the best performance and system robustness.

Automated summary

Renewable electricity generation, particularly wind usage, is being increasingly requested to replace other fuels. This study aimed to develop a suitable speed control method for wind power systems that maximizes power generation while reducing mechanical loads. The research compared different tuning methods to determine the most effective controller in terms of performance and system robustness.