Modeling and Robust Control with Wind Speed Estimation by Artificial Neural Networks of a DFIG Wind Turbine Under Both Normal Operation and Grid Fault

2017 ◽  
Vol 12 (2) ◽  
pp. 100
Author(s):  
Anass Bakouri ◽  
Hassane Mahmoudi ◽  
Ahmed Abbou
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Robust Control ◽  
Wind Speed ◽  
Wind Turbine ◽  
Normal Operation ◽  
Speed Estimation ◽  
Artificial Neural

An Efficient Hybrid Forecasting Approach for Wind Speed Time Series

2017 ◽  
Vol 7 (9) ◽  
pp. 13
Author(s):  
Bhargavi Munnaluri ◽  
K. Ganesh Reddy
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Wind Speed ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Support Vector ◽  
Hybrid Forecasting ◽  
Artificial Neural ◽  
Future Utilization

Wind forecasting is one of the best efficient ways to deal with the challenges of wind power generation. Due to the depletion of fossil fuels renewable energy sources plays a major role for the generation of power. For future management and for future utilization of power, we need to predict the wind speed.  In this paper, an efficient hybrid forecasting approach with the combination of Support Vector Machine (SVM) and Artificial Neural Networks(ANN) are proposed to improve the quality of prediction of wind speed. Due to the different parameters of wind, it is difficult to find the accurate prediction value of the wind speed. The proposed hybrid model of forecasting is examined by taking the hourly wind speed of past years data by reducing the prediction error with the help of Mean Square Error by 0.019. The result obtained from the Artificial Neural Networks improves the forecasting quality.

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