Abstract: The disclosure relates to a compact design method for inductive filtering transformer. According to the disclosure, the inductive filtering transformer adopts an approximative zero-impedance design under the premise of satisfying the filtering performance. The direct optimization objective is to minimize the distance from the transformer core center to the outermost winding. This disclosure balances the constraint of winding impedance matching and transformer size optimization and satisfies the application of limited installation space.

Abstract: The invention provides a probabilistic wind speed forecasting method and system based on multi-scale information. First, a convolutional neural network (CNN) model with multiple convolutional layers is employed for extracting multi-scale features (MSFs). Then, an attention-based long short-term memory (LSTM) is utilized to extract temporal characteristics from the features at each scale and encode them into a low-dimensional feature vector. The difference between the conditional quantiles of adjacent quantiles is obtained with the proposed non-crossing quantile loss, and the estimates of all the conditional quantiles can be calculated by accumulating and subtracting. The proposed invention can extract sufficient MSFs from limited data, provide high-quality and reliable probabilistic forecasts, and solve the crossing problem of quantile-based models.

Abstract: The invention provides a wind power forecasting method and system based on an asymmetric Laplace distribution. It utilizes the asymmetric Laplace distribution to model the uncertainty of the power forecasts. First, the maximum information coefficient (MIC) is used to characterize the linear and nonlinear relationship between the target and historical power data to select reasonable and optimal inputs. Then, to avoid the information loss, a multi-scale feature fusion module is proposed which combines the features obtained from different convolutional layers of a convolutional neural network (CNN), thereby further enhancing the feature extraction ability of the traditional CNN. Finally, a BiLSTM is used to extract temporal information and forecast the parameters of asymmetric Laplace distribution.

Abstract: The disclosure relates to a compact design method for inductive filtering transformer. According to the disclosure, the inductive filtering transformer adopts an approximative zero-impedance design under the premise of satisfying the filtering performance. The direct optimization objective is to minimize the distance from the transformer core center to the outermost winding. This disclosure balances the constraint of winding impedance matching and transformer size optimization and satisfies the application of limited installation space.