Junhua Zhao

TL;DR: In this article, a new method to determine an optimal DG allocation is presented which is based on the modal analysis that involves eigenvalue and eigenvectors techniques, and a numerical example on a 34-bus distribution network is given to illustrate the effectiveness of the proposed method.

TL;DR: Simulation results show that the data-driven model is more accurate than the analytical model in predicting system states, while the proposed control method outperforms the original analytical method.

TL;DR: In this paper, a novel approach of designing the optimal bidding strategies based on incomplete market information is proposed, which predicts the expected bidding productions of each rival generator in the market based on publicly available bidding data, and the non-linear relationship between generators' bidding productions and the market clearing price is also estimated from historical bidding and price data, using support vector machine (SVM).

TL;DR: It has shown that ELM (Extreme Learning Machine) algorithm has a superior performance in prediction of price spikes compared with other existing classification algorithms such as SVM (Support Vector Machine).

TL;DR: In this paper , the authors demonstrate the disturbing potential of these generative models in synthesizing fake images, plagiarizing existing images, and deliberately modifying images and reveal vast risks and arouses the vigilance of the scientific community on fake scientific images generated by artificial intelligence models.