Supervised artificial neural networks by field-oriented control applied to PMSG-based variable-speed wind turbine

Artificial intelligence techniques (AI), especially Artificial Neural Networks (ANN), have a significant impact on the control of electrical machines and power systems. Neural networks have created new and advanced boundaries in controlling power systems and electric generators, and they are already a complex, multidisciplinary technology undergoing dynamic evolution in recent years. This paper presents the application of a control methodology to a permanent magnet synchronous generator (PMSG) in the context of a variable speed wind turbine system. The study particularly focused on exploring the performance and optimization of the wind turbine system through the use of this control approach to extract the maximum amount of variable speed wind energy and address associated errors and problems, the control being studied and applied is Supervised ANN by FOC. The discussion explored the various components and performance of a wind system (WS) through simulations conducted in MATLAB/Simulink.