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您的检索式：作者名="Xiangjing Su"
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| 1 | Release characteristics of alkali and alkaline earth metallic species during biomass pyrolysis and steam gasification process显示文摘 | JIANG L HU S XIANGJ SU S SUN L S XU K YAO Y | 2012 | Bioresour Technol2012，116，： | 1 |
| 2 | Sparse Adversarial Learning for FDIA Attack Sample Generation in Distributed Smart显示文摘False data injection attack(FDIA)is an attack that affects the stability of grid cyber-physical system(GCPS)by evading the detecting mechanism of bad data.Existing FDIA detection methods usually employ complex neural networkmodels to detect FDIA attacks.However,they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse,making it difficult for neural network models to obtain sufficient samples to construct a robust detection model.To address this problem,this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge,which can effectively bypass the detectionmodel to threaten the power grid system.A generative adversarial network(GAN)framework is first constructed by combining residual networks(ResNet)with fully connected networks(FCN).Then,a sparse adversarial learning model is built by integrating the time-aligned data and normal data,which is used to learn the distribution characteristics between normal data and attack data through iterative confrontation.Furthermore,we introduce a Gaussian hybrid distributionmatrix by aggregating the network structure of attack data characteristics and normal data characteristics,which can connect and calculate FDIA data with normal characteristics.Finally,efficient FDIA attack samples can be sequentially generated through interactive adversarial learning.Extensive simulation experiments are conducted with IEEE 14-bus and IEEE 118-bus system data,and the results demonstrate that the generated attack samples of the proposed model can present superior performance compared to state-of-the-art models in terms of attack strength,robustness,and covert capability. | Fengyong Li Weicheng Shen Zhongqin Bi Xiangjing Su | 2024 | Computer Modeling in Engineering & Sciences2024，139，5： | 0 |
| 3 | Multi-timescale Energy Sharing with Grid-BESS Capacity Rental Considering Uncertainties显示文摘The growing number of distributed energy resources(DERs)in distribution networks brings new opportunities for local energy sharing.This paper proposes a multi-timescale en-ergy sharing approach among DER aggregators and distribution system operators(DSOs)considering grid-battery energy storage system(BESS)capacity rental and network operations.An energy sharing coordinator is created to manage the energy sharing with price determination.In an hour-ahead stage,the buying/selling energy and required grid-BESS rental capacity are optimally determined by the aggregators while the network operation is robustly considered by the DSO.In addition to renewable generation and loads,the power exchanges of the aggregators are treated as uncertainties.Then during each hour,15-min-ahead energy transaction and controllable DERs are optimized to track uncertainty realization.The uncertainties in the aggregators and the DSO are addressed by stochastic and robust optimization methods,respectively.To efficiently solve the proposed energy sharing problem,a distributed solution algorithm with step length control and step reduction techniques is developed.The simulation results verify the high efficiency of the proposed energy sharing approach.Index Terms-Battery capacity rental,distributed solution algorithm,energy sharing,optimization,uncertainty. | Bo Wang Cuo Zhang Chaojie Li Xiangjing Su Zihang Qiu Zhao Yang Dong | 2023 | CSEE Journal of Power and Energy Systems2023，9，4： | 0 |
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