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In this work, a neural controller for wind turbine pitch control is presented. The controller is based on a radial basis function (RBF) network with unsupervised learning algorithm. The RBF network uses the error between the output power and the rated power and its derivative as inputs, while the integral of the error feeds the learning algorithm. A performance analysis of this neurocontrol strategy is carried out, showing the influence of the RBF parameters, wind speed, learning parameters, and control period, on the system response. The neurocontroller has been compared with a proportional-integral-derivative (PID) regulator for the same small wind turbine, obtaining better results. Simulation results show how the learning algorithm allows the neural network to adjust the proper control law to stabilize the output power around the rated power and reduce the mean squared error (MSE) over time.

Performance Analysis of a Wind Turbine Pitch Neurocontroller with Unsupervised Learning