The wind turbine's main bearing and its replacement are extremely costly. However, the main bearing is found to be electrically burned after a few years of operation. In this paper, a new pattern of the breakdown mechanism, the bearing current induced by electrostatic discharge (ESD), is studied. The analysis and test results prove that the ESD effect in wind turbines is a non-negligible source of the main shaft bearing current in wind turbines.

This study improves wind power forecasting accuracy with machine learning. A hybrid approach of Empirical Mode Decomposition (EMD) and Support Vector Regression (SVR), with a hyperparameter tuning, leads to higher accuracy. The wind turbine power data used and found that EMD-based approach is better than SVR. Applying parameter tuning showed significant improvement in wind power forecasting. The study's findings are important for energy traders looking to maximize profits in the intraday market.