Estimation of wind energy power using different artificial intelligence techniques and empirical equations

The understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems.