<![CDATA[Variable-speed wind turbines with Permanent Magnet Synchronous Generator (PMSG) offer high efficiency in converting wind energy into electrical power. However, under wind conditions exceeding the rated speed, a pitch angle control mechanism is required to maintain output power stability and protect the system from overloading. Pitch angle control is commonly implemented using conventional PI controllers. This study aims to implement a single-neuron PI controller as an adaptive solution for regulating the pitch angle in a PMSG-based wind turbine system. The proposed control method combines the basic structure of a PI controller with adaptive learning based on a single neuron, enabling real-time adjustment of the controller weights based on output power error. The simulation and modeling of the wind energy conversion system are carried out using MATLAB/Simulink, including the modeling of the wind turbine, PMSG, conventional PI-based pitch control, and single-neuron PI-based pitch control. Simulation results demonstrate that the single-neuron PI controller effectively maintains the output power close to the reference value, with minimal deviation and high stability across various wind speeds exceeding the rated condition. Compared to the conventional PI controller, this method offers superior adaptability to system parameter variations and dynamic wind speed changes.]]>
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