Development of Reduced Feeder and Load Models Using Practical Topological and Loading Data

Distribution feeder and load model reduction methods are essential for maintaining a good tradeoff between accurate representation of grid behavior and reduced computational complexity in power system studies. An effective algorithm to obtain a reduced order representation of the practical feeders using utility topological and loading data has been presented in this paper. Simulations conducted in this work show that the reduced feeder and load model of a utility feeder, obtained using the proposed method, can accurately capture contactor and motor stalling behaviors for critical events such as fault induced delayed voltage recovery.

SegAttnGAN: Text to Image Generation with Segmentation Attention

In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task. As the segmentation data introduced to the model provides useful guidance on the generator training, the proposed model can generate images with better realism quality and higher quantitative measures compared with the previous state-of-art methods. We achieved Inception Score of 4.84 on the CUB dataset and 3.52 on the Oxford-102 dataset. Besides, we tested the self-attention SegAttnGAN which uses generated segmentation data instead of masks from datasets for attention and achieved similar high-quality results, suggesting that our model can be adapted for the text-to-image synthesis task.