A continuous actor-critic Maximum Power Point Tracker applied to low power wind turbine systems

This paper aims to present an application of a continuous states and actions actor-critic algorithm based on artificial neural networks function approximations, capable of tracking the maximum power point of a wind turbine system. The application does not use any mechanical sensor for wind. The proposed system is composed of a three phasic permanent magnet synchronous generator of 1 kW, a diode rectifier, a Boost converter and a resistive load. The algorithm consists of two Multi-layer Perceptron Neural Networks (NN) witch approximate the state value function and the DC bus voltage reference, the power signal feedback adjusts NN parameters through backpropagation algorithm. To validate the proposed algorithm, simulations using average models of the system are made on Simulink/Matlab software with different wind speed profiles. A wind turbine emulator test bench using a DC motor to emulate the torque made by the wind on the generator rotor provides experimental results Over more dynamic wind profiles tested, the proposed MPPT algorithm response overcomes the classic P&O.