Sensorless estimation of wind speed by adaptive neuro-fuzzy methodology

The wind speed has a huge impact on the wind turbine output energy and safety. Because of this, many control algorithms use a measure of the wind speed to increase performance. Unfortunately, no precise measurement of the effective wind speed is online available from direct measurements, which means that it must be estimated in order to make such control methods applicable in practice. In this paper, a novel algorithm for wind speed estimation in wind-power generation systems is proposed, which is based on adaptive neuro-fuzzy inference system (ANFIS). The inputs of the ANFIS wind speed estimator are chosen as the wind turbine power coefficient, rotational speed and blade pitch angle. During the offline training, a specified model, which relates the inputs to the output, is obtained. Then, the wind speed is determined online from the instantaneous inputs. Neural network in ANFIS adjusts parameters of membership function in the fuzzy logic of the fuzzy inference system (FIS). This intelligent estimator is implemented using Matlab/Simulink and the performances are investigated. The simulation results presented in this paper show the effectiveness of the developed method.