Post-Disturbance Dynamic Frequency Features Prediction Based on Convolutional Neural Network

The significant imbalance between power generation and load caused by severe disturbance may make the power system unable to maintain a steady frequency. If the post-disturbance dynamic frequency features can be predicted and emergency controls are appropriately taken, the risk of frequency instability will be greatly reduced. In this paper, a predictive algorithm for post-disturbance dynamic frequency features is proposed based on convolutional neural network (CNN) . The operation data before and immediately after disturbance is used to construct the input tensor data of CNN, with the dynamic frequency features of the power system after the disturbance as the output. The operation data of the power system such as generators unbalanced power has spatial distribution characteristics. The electrical distance is presented to describe the spatial correlation of power system nodes, and the t-SNE dimensionality reduction algorithm is used to map the high-dimensional distance information of nodes to the 2-D plane, thereby constructing the CNN input tensor to reflect spatial distribution of nodes operation data on 2-D plane. The CNN with deep network structure and local connectivity characteristics is adopted and the network parameters are trained by utilizing the backpropagation-gradient descent algorithm. The case study results on an improved IEEE 39-node system and an actual power grid in USA shows that the proposed method can predict the lowest frequency of power system after the disturbance accurately and quickly.

Impact of Junction Depth and Abruptness on the Activation and the Leakage Current in Germanium n$^{+}$/p Junctions

The phosphorous activation in Ge n$^{+}$/p junctions is compared in terms of junction depth, by using laser spike annealing at 860{deg}C for 400$mu$s. The reverse junction leakage is found to strongly depend on the abruptness of dopant profiles. A shallow and abrupt junction is shown to have lower phosphorous activation level, due to surface dose loss, and higher band-to-band tunneling (BTBT) leakage, when compared to the deep junction. Simulations were carried out to evaluate the lowest achievable OFF-state currents (I$_{OFF}$) for Ge double-gate FETs when using such an abrupt junction. Our results indicate that a Ge body thickness smaller than 5 nm is required to suppress the BTBT leakage and meet the requirement for the high performance devices defined by the International Technology Roadmap for Semiconductors (I$_{OFF}$ = 10$^{-7}$ A/$mu$m).