A neural adaptative step size method for maximum power point tracking in low power wind turbine systems

This paper aims to present an algorithm based on artificial neural networks, with Multilayer Perceptron architecture, capable of tracking the maximum power point of a wind turbine system: (i) without the use of any mechanical wind speed sensors and (ii) with less oscillatory steady state. The proposed method it is based on the traditional Perturb & Observe algorithm. However, it uses the neural network to learn a strategy to define the best action (the update step size) for each operation point. The learning is done through backpropagation. To validate the proposed algorithm, it is shown simulations made on Simulink/Matlab software. The simulations consists of a generic wind turbine of 1kW. The average values of generated electric power are the performance index.