Wind and mechanical speed estimators using Neural Networks for MPPT applied to a WECS

This paper presents two Neural Networks (NN) that estimate the wind speed and optimal mechanical speed. These use the Tip Speed Ratio (TSR) technique for Maximum Power Point Tracking (MPPT) in a simulated Wind Energy Conversion System (WECS) connected to the grid. The purpose of the NNs estimators is to provide the appropriate mechanical speed reference on the speed control loop despite the random behavior of the wind, avoiding the use of an anemometer from the cut-in speed. This is achieved by estimating the wind speed from the power of the wind turbine (WT) and mechanical speed of the permanent magnet synchronous machine (PMSM). The performance of the control applied to the converters is evaluated through a simulation in MATLAB/Simulink. The stability of the machine-side converter (MSC) and the grid-side converter (GSC) systems is demonstrated by applying a stepped wind speed profile and a random wind speed profile.