  The wind power ramp events threaten the power grid safety significantly. To
improve the ramp prediction accuracy, a hybrid wavelet deep belief network
algorithm with adaptive feature selection (WDBNAFS) is proposed. First, the
wind power characteristic is analyzed. Then, wavelet decomposition is addressed
to the time series, and an adaptive feature selection algorithm is proposed to
select the inputs of the prediction model. Finally, a deep belief network is
employed to predict the wind power ramp event, and the proposed WDBNAFS was
testified with the experiments based on the practical data. The simulation
results demonstrate that the prediction accuracy of the proposed algorithm is
more than 90%.
