检索结果-南通市图书馆

CSEE Journal of Power and Energy Systems 2025年第1期11卷 136-149页

作者： tianjing wang Yong Tang School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing100192China

This study proposes a new transient-stability preventive control (TSPC) method based on graph convolutional neural networks (GCNN) and transfer deep reinforcement learning (DRL) to address non-convergence problems of traditional optimization algorithms and slow training speed of artificial intelligence algorithms for TSPC. First, a transient stability assessor (TSA) with GCNN is developed to assess current-power-flow state. Sensitivities of the transient-stability index relative to the generators are approximately calculated using TSA;generators with significant influence able to narrow action space are identified. Subsequently, the Markov decision-making process of TSPC is derived by introducing the process of TSPC. A DRL for TSPC is constructed by adding entropy to twin delayed deep deterministic policy gradient (TD3). Knowledge learned by TSA is transferred to DRL based on transfer learning, which improves learning efficiency. Finally, case studies based on the IEEE 39-bus system and an actual power grid prove the effectiveness of the proposed method. Comparisons performed with reference algorithms in literature demonstrate the proposed method has better performance in both control effect and speed.

关键词： Deep reinforcement learning graph convolution preventive control transfer transient stability

Automatic Voltage Control of Differential Power Grids Based on Transfer Learning and Deep Reinforcement Learning

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CSEE Journal of Power and Energy Systems 2023年第3期9卷 937-948页

作者： tianjing wang Yong Tang Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research InstituteBeijing 100192China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore

In terms of model-free voltage control methods,when the device or topology of the system changes,the model’s accuracy often decreases,so an adaptive model is needed to coordinate the changes of *** overcome the defects of a model-free control method,this paper proposes an automatic voltage control(AVC)method for differential power grids based on transfer learning and deep reinforcement ***,when constructing the Markov game of AVC,both the magnitude and number of voltage deviations are taken into account in the ***,an AVC method based on constrained multiagent deep reinforcement learning(DRL)is *** further improve learning efficiency,domain knowledge is used to reduce action ***,distribution adaptation transfer learning is introduced for the AVC transfer circumstance of systems with the same structure but distinct topological relations/parameters,which can perform well without any further training even if the structure ***,for the AVC transfer circumstance of various power grids,parameter-based transfer learning is created,which enhances the target system’s training speed and ***,the method’s efficacy is tested using two IEEE systems and two real-world power grids.

关键词： Deep reinforcement learning differential power grids transfer voltage control

Edge Intelligence for Smart Grid:A Survey on Application Potentials

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

CSEE Journal of Power and Energy Systems 2023年第5期9卷 1623-1640页

作者： Hoay Beng Gooi tianjing wang Yong Tang Nanyang Technological University Singapore 639798Singapore China Electric Power Research Institute Beijing 100092China

With the booming of artificial intelligence(AI),Internet of Things(IoT),and high-speed communication technology,integrating these technologies to innovate the smart grid(SG)further is future development direction of the power *** by this trend,billions of devices in the SG are connected to the Internet and generate a large amount of data at network *** reduce pressure of cloud computing and overcome defects of centralized learning,emergence of edge computing(EC)makes the computing task transfer from the network center to the network *** further exploring the relationship between EC and AI,edge intelligence(EI)has become one of the research *** of EI in flexibly utilizing EC resources and improving AI model learning efficiency make its application in SG a good ***,since only a few existing studies have applied EI to SG,this paper focuses on the application potential of EI in ***,the concepts,characteristics,frameworks,and key technologies of EI are ***,a comprehensive review of AI and EC applications in SG is ***,application potentials for EI in SG are explored,and four application scenarios of EI for SG are ***,challenges and future directions for EI in SG are *** application survey of EI on SG is carried out before EI enters the largescale commercial stage to provide references and guidelines for developing future EI frameworks in the SG paradigm.

关键词： Artificial intelligence edge computing edge intelligence federated learning smart grid

Fouling corrosion in aluminum heat exchangers

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Aeronautics 2015年第3期28卷 954-960页

作者： Su Jingxin Ma Minyu wang tianjing Guo Xiaomei Hou Liguo wang Zhiping Tianjin Key Laboratory for Civil Aircraft Airworthiness and Maintenance Civil Aviation University of China Aircraft Maintenance and Engineering Corporation Beijing Capital International Airport

Abstract Fouling deposits on aluminum heat exchanger reduce the heat transfer efficiency and cause corrosion to the apparatus. This study focuses on the corrosive behavior of aluminum cou- pons covered with a layer of artificial fouling in a humid atmosphere by their weight loss, Tafel plots, electrochemical impedance spectroscopy （EIS）, and scanning electron microscope （SEM） observations. The results reveal that chloride is one of the major elements found in the fouling which damages the passive film and initiates corrosion. The galvanic corrosion between the metal and the adjacent carbon particles accelerates the corrosive process. Furthermore, the black carbon favors the moisture uptake, and gives the dissolved oxygen greater chance to migrate through the fouling layer and form a continuous diffusive path. The corrosion rate decreasing over time is conformed to electrochemistry measurements and can be verified by Faraday＇s law. The EIS results indicate that the mechanism of corrosion can be interpreted by the pitting corrosion evolution mechanism, and that pitting was observed on the coupons by SEM after corrosive exposure.

关键词： Aluminum EIS Heat exchanger Pitting corrosion Tafel plots

Global Dynamics in a Predator-Prey Model with Allee Effect

在线全文

学校读者我要写书评

暂无评论

Journal of Applied Mathematics and Physics 2024年第7期12卷 2377-2385页

作者： tianjing wang Fuqin Sun School of Sciences Tianjin University of Technology and Education Tianjin China

Since the last century, various predator-prey systems have garnered widespread attention. In particular, the predator-prey systems have sparked significant interest among applied mathematicians and ecologists. From the perspectives of both mathematics and biology, a predator-prey system with the Allee effect and featuring the Bazykin functional response has been established. For this model, analyses have been conducted on its boundedness, the properties of its solutions, the existence of equilibrium points, as well as its local stability and Hopf bifurcations.

关键词： Bazykin Allee Effect Hopf Bifurcation

Artificial intelligence driving perception,cognition,decision‐making and deduction in energy systems:State‐of‐the‐art and potential directions

在线全文

学校读者我要写书评

暂无评论

Energy Internet 2024年第1期1卷 27-33页

作者： ZhaoYang Dong tianjing wang Department of Electrical Engineering City University of Hong KongKowloonHong KongChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore

In the context of energy systems,managing the complex interplay between diverse power sources and dynamic demands is *** a focus on smart grid technology,continuously innovating artificial intelligence(AI)algorithms,such as deep learning,reinforcement learning,and large language model technologies,have been or have the potential to be leveraged to predict energy consumption patterns,enhance grid operation,and manage distributed energy resources *** capabilities are essential to meet the requirements of perception,cognition,decision‐making,and deduction in en-ergy ***,there are some critical challenges in efficiency,interpretability,transferability,stability,economy,and *** overcome these challenges,we propose critical potential directions in future research,including reasonable sample generation,training models with small datasets,enhancing transfer ability,combining with physics models,collective generative pre‐trained transformer‐agents,multiple foundation models,and improving system robustness,to make advancing AI technologies more suitable for practical engineering.

关键词： artificial intelligence cognition decision‐making deduction energy systems perception

A Mobile-Agent-Based Adaptive Data Fusion Algorithm for Multiple Signal Ensembles in Wireless Sensor Networks

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Wireless Sensor Network 2009年第5期1卷 458-466页

作者： tianjing wang Zhen YANG Guoqing LIU 不详

Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.

关键词： Wireless Sensor Networks Mobile Agent Compressed Sensing Distributed Compressed Sensing Joint Sparsity Joint Reconstruction

Interaction between Mnk2 and CBC^(VHL) ubiquitin ligase E3 complex

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Life Sciences) 2006年第3期49卷 265-273页

作者： wang Pingzhang, wang Xin, wang Feng, CAI tianjing & LUO Ying Chinese National Human Genome Center, Beijing 100176, China Institute of Basic Medical Sciences, Chinese Acadamy of Medical Sciences, Beijing 100005, China Shanghai Genomics Inc., Shanghai 201203, China Chinese National Human Genome Center Beijing China Institute of Basic Medical Sciences Chinese Acadamy of Medical Sciences Beijing China Shanghai Genomics Inc. Shanghai China

MAP kinase-interacting kinase-2 (Mnk2) is one of the downstream kinasesactivated by MAP kinases. It phosphorylates the eukaryotic initiation factor 4E (elF4E), althoughthe role of elF4E phosphorylation and the role of Mnk2 in the process of proteintranslation are notwell understood. Except for elF4E, other physiological substrates of Mnk2 are still unidentified. Tolook for these unidentified substrates and to reveal the physiological function of Mnk2, weperformed a yeast two-hybrid screening with Mnk2 as the bait. The results demonstrated Mnk2 couldinteract with VHL (von Hippel-Lindau tumor suppressor), Rbx1 (ring-box1) and Cul2 (Cullin2) proteinsin yeast cells. Furthermore, we validated the interaction between Mnk2 and VHL proteins inmammalian cells by co-immunoprecipitation analysis. Because the three proteins VHL, Rbx1 and Cul2are all components of the CBC^(VHL) ubiquitin ligase E3 complex, it has been shown that Mnk2 caninteract with CBC^(VHL) complex, and is probably one of the newsubstrates of the CBC^(VHL) ***, during the interaction of Mnk2 with von Hippel-Lindau (VHL) tumor suppressor- bindingprotein 1 (VBP1), it appears that Mnk2 also joins to modulate cell shape as VBP1 plays an importantrole in the process of the maturation of the cytoskeleton and in the process of morphogenesis.

关键词： Mnk2, yeast two-hybrid, CBCVHL ubiquitin ligase E3 complex, VBP1.

交直流混合微电网网络坚强度评估的指标体系及方法

在线全文

学校读者我要写书评

暂无评论

电网技术 2018年第2期42卷 455-462页

作者：朱永强王甜婧新能源电力系统国家重点实验室(华北电力大学)

网络坚强度是交直流混合微电网构建与运行的基础,可体现系统的容量、结构合理性以及抗干扰能力。在定义网络坚强度和描述其表现的基础上,提出了一套交直流混合微电网的网络坚强度评估指标体系。该体系基于系统对坚强度的需求层次,衍生... 详细信息

网络坚强度是交直流混合微电网构建与运行的基础,可体现系统的容量、结构合理性以及抗干扰能力。在定义网络坚强度和描述其表现的基础上,提出了一套交直流混合微电网的网络坚强度评估指标体系。该体系基于系统对坚强度的需求层次,衍生出结构、容量、联络程度、备用大小和故障转移能力等相关指标,考虑了节点和支路的分布情况、节点和支路的供电能力以及交流和直流微网的容载比等因素。同时提出了可体现数据独立性和波动性的独立信息熵权法,并运用基于独立信息熵权-层次分析-主成分分析（independent information entropy-analytic hierarchy process-principal component analysis,IIE-AHP-PCA）的分层组合评价模型对网络坚强度进行评估。通过算例证明,所提出的指标体系与评价方法合理有效。

关键词：交直流混合微电网坚强度评估独立信息熵权法指标体系

在线全文

学校读者我要写书评

暂无评论

采用分组数据的序贯频谱感知方法

西安交通大学学报 2015年第12期49卷 34-39,150页

作者：王天荆姜华刘国庆南京工业大学信息与计算科学系南京210009

针对传统的认知无线电频谱感知方法在低信噪比下感知时间长、系统吞吐量低的问题,提出了一种采用分组数据的混合型序贯检测(MSD)方法。该方法首先将次用户的感知数据进行分组形成超采样,然后由数学理论推导出最大化系统吞吐量的最优虚... 详细信息

针对传统的认知无线电频谱感知方法在低信噪比下感知时间长、系统吞吐量低的问题,提出了一种采用分组数据的混合型序贯检测(MSD)方法。该方法首先将次用户的感知数据进行分组形成超采样,然后由数学理论推导出最大化系统吞吐量的最优虚警概率,并且利用牛顿迭代法搜索最优虚警概率,最后在最优虚警概率下对超采样序列依次进行细检测和粗检测,快速获得检测结果。MSD方法采用分组数据进行频谱感知,能有效缩短感知时长,获得最大系统吞吐量,从而提高频谱利用率。蒙特卡罗仿真结果表明,在低信噪比下MSD方法比传统的序贯检测法和序贯能量检测法的平均归一化吞吐量增加了109%和21%,平均感知开销率减少了75%和49%。

关键词：认知无线电频谱感知序贯检测超采样