Effect of annealing on the electrical performance of N-polarity GaN Schottky barrier diodes

A nitrogen-polarity(N-polarity)GaN-based high electron mobility transistor(HEMT)shows great potential for high-fre-quency solid-state power amplifier applications because its two-dimensional electron gas(2DEG)density and mobility are mini-mally affected by device scaling.However,the Schottky barrier height(SBH)of N-polarity GaN is low.This leads to a large gate leakage in N-polarity GaN-based HEMTs.In this work,we investigate the effect of annealing on the electrical characteristics of N-polarity GaN-based Schottky barrier diodes(SBDs)with Ni/Au electrodes.Our results show that the annealing time and tem-perature have a large influence on the electrical properties of N-polarity GaN SBDs.Compared to the N-polarity SBD without annealing,the SBH and rectification ratio at±5 V of the SBD are increased from 0.51 eV and 30 to 0.77 eV and 7700,respec-tively,and the ideal factor of the SBD is decreased from 1.66 to 1.54 after an optimized annealing process.Our analysis results suggest that the improvement of the electrical properties of SBDs after annealing is mainly due to the reduction of the inter-face state density between Schottky contact metals and N-polarity GaN and the increase of barrier height for the electron emis-sion from the trap state at low reverse bias.

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems,ranging from pattern recognition to signal processing.The data in these tasks are typically represented in Euclidean domains.Nevertheless,there is an increasing number of applications in power systems,where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes.The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains.Recently,many publications generalizing deep neural networks for graph-structured data in power systems have emerged.In this paper,a comprehensive overview of graph neural networks(GNNs)in power systems is proposed.Specifically,several classical paradigms of GNN structures,e.g.,graph convolutional networks,are summarized.Key applications in power systems such as fault scenario application,time-series prediction,power flow calculation,and data generation are reviewed in detail.Furthermore,main issues and some research trends about the applications of GNNs in power systems are discussed.

Fault Diagnosis of Power Transformers Using Graph Convolutional Network

Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics.Their accuracy is limited,since they are hard to learn from other algorithms to improve their own performance.To improve the accuracy of transformer fault diagnosis,a novel method for transformer fault diagnosis based on graph convolutional network(GCN)is proposed.The proposed method has the advantages of two kinds of existing methods.Specifically,the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples.Furthermore,the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type.The back propagation algorithm is used to complete the training process of GCN.The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network,multi-layer perceptron,support vector machine,extreme gradient boosting tree,k-nearest neighbors and Siamese network in different input features and data volumes,which can effectively meet the needs of diagnostic accuracy.

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

High-quality datasets are of paramount importance for the operation and planning of wind farms.However,the datasets collected by the supervisory control and data acquisition(SCADA)system may contain missing data due to various factors such as sensor failure and communication congestion.In this paper,a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder(CE),which consists of an encoder,a decoder,and a discriminator.Through deep convolutional neural networks,the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly.The proposed method can not only fully use the surrounding context information by the reconstructed loss,but also make filling data look real by the adversarial loss.In addition,the correlation among multiple missing attributes is taken into account by adjusting the format of input data.The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks,large valleys,and fast ramps.Moreover,the CE shows stronger generalization ability than traditional methods such as auto-encoder,K-means,k-nearest neighbor,back propagation neural network,cubic interpolation,and conditional generative adversarial network for different missing data scales.

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.