ANFIS-BASED REAL-TIME POWER ESTIMATION FOR WIND TURBINES

Abstract

In this study, it is aimed to make real-time power estimation for the V44-600 model wind turbine of Vestas company. The scope of the study is aimed to perform ANFIS-based power estimation for the V44-600 VESTAS wind turbine, which is intensely used in the wind industry, by using the wind speed and air density data of the city of Nevşehir. For this purpose, an Adaptive Network Based Fuzzy Inference System (ANFIS) trained on V44-600 wind turbine data was used. For the training and testing steps of ANFIS, wind speed, air density, and output power of the wind turbine are used as input-output parameters. As a result of the simulations and training, the percent relative error value in the widest range where the prediction value deviates from the true value is 11.86%. This value was higher than expected due to the scarcity of the data used in the ANFIS training (144) and the repetitive values in the output power. Similarly, the lowest efficiency value is 89.4%. Despite all this, it has been observed that ANFIS gives good results if the data used in the testing process is within the scope of the data used in the training. Moreover, the developed model when supported with 32-bit hardware can make real-time power estimation for a real wind turbine. The main motivation for this study; is develop a model that can predict the output power for the Vestas V44-600 model based on wind speed and air density data. In addition, it is to produce the Fuzzy Interface System (FIS) file that enables the developed model to run on embedded systems.