Neural Network-Based Real-Time Wind Energy Estimation

This work proposes a real-time wind power estimation approach for a direct-current (DC) wind energy conversion system based on an artificial neural network (ANN). The proposed ANN uses the Levenberg–Marquardt (LM) algorithm and is trained using experimental data collected from a LabVolt wind energy test bench. The objective is to accurately estimate the instantaneous electrical power generated from the measured wind speed under different operating conditions. The proposed methodology comprises experimental data acquisition, ANN training, algorithm comparison, and model validation. The training performance of the LM algorithm was compared with that of the Resilient Backpropagation algorithm using the same dataset and network architecture. The results demonstrate that the LM algorithm provides superior convergence and prediction accuracy for the considered dataset. The selected ANN model achieved an MSE of 0.240601, an RMSE of 0.490511, an MAE of 0.207092, and a coefficient of determination of (R2 = 0.999964). These results demonstrate an excellent agreement between the measured and predicted power values. The proposed ANN-based approach provides an accurate and computationally efficient solution for real-time wind power estimation and shows strong potential for integration into intelligent energy management systems and digital twin frameworks for wind energy conversion systems.