检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

488 篇 会议
76 篇 期刊文献

馆藏范围

564 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

334 篇 工学
- 273 篇 电气工程
- 110 篇 动力工程及工程热...
- 110 篇 计算机科学与技术...
- 81 篇 控制科学与工程
- 80 篇 软件工程
- 72 篇 电子科学与技术（可...
- 50 篇 安全科学与工程
- 28 篇 机械工程
- 25 篇 信息与通信工程
- 21 篇 交通运输工程
- 21 篇 环境科学与工程（可...
- 17 篇 石油与天然气工程
- 14 篇 化学工程与技术
- 13 篇 材料科学与工程（可...
- 12 篇 力学（可授工学、理...
- 10 篇 仪器科学与技术
- 10 篇 生物工程
165 篇 理学
- 78 篇 数学
- 61 篇 物理学
- 22 篇 统计学（可授理学、...
- 21 篇 系统科学
- 15 篇 化学
- 11 篇 大气科学
- 11 篇 地球物理学
- 10 篇 生物学
61 篇 管理学
- 53 篇 管理科学与工程(可...
- 26 篇 工商管理
- 8 篇 图书情报与档案管...
14 篇 经济学
- 14 篇 应用经济学
10 篇 法学
- 10 篇 社会学
3 篇 医学
1 篇 农学

主题

38 篇 power system sta...
24 篇 power grids
23 篇 stability analys...
22 篇 real-time system...
22 篇 accuracy
21 篇 simulation
19 篇 analytical model...
18 篇 transient analys...
16 篇 predictive model...
16 篇 voltage control
16 篇 power system dyn...
16 篇 data models
14 篇 wind power gener...
14 篇 control systems
13 篇 power systems
13 篇 feature extracti...
13 篇 renewable energy...
12 篇 wind power
11 篇 hvdc transmissio...
11 篇 wind turbines

机构

87 篇 guangdong provin...
56 篇 csg key laborato...
53 篇 state key labora...
43 篇 national energy ...
37 篇 key laboratory o...
31 篇 state key labora...
22 篇 state grid shand...
18 篇 key laboratory o...
15 篇 nari technology ...
15 篇 nari group corpo...
15 篇 state grid shand...
15 篇 nari group corpo...
13 篇 guangdong provin...
12 篇 state grid jinan...
11 篇 electric power r...
10 篇 china southern p...
10 篇 shandong univers...
10 篇 national energy ...
9 篇 state grid elect...
9 篇 china southern p...

作者

34 篇 yihua zhu
31 篇 huang libin
28 篇 zhu yihua
22 篇 libin huang
21 篇 guo haiping
19 篇 zhang jie
18 篇 lu yuanhong
17 篇 qi guo
16 篇 guo qi
16 篇 liang tu
16 篇 haiping guo
15 篇 jie zhang
15 篇 tu liang
14 篇 yuanhong lu
14 篇 binjiang hu
14 篇 zhang wei
13 篇 zhang jingyue
13 篇 wei cong
12 篇 cai haiqing
12 篇 hu binjiang

语言

538 篇 英文
18 篇 其他
8 篇 中文

检索条件"机构=State Key Laboratory of Intelligent Power Grid Protection and Operation Control"

共 564 条记录，以下是31-40 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Application of deep reinforcement learning in power system scheduling strategy simulation 4

Application of deep reinforcement learning in power system s...

引用

4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Hu, Binjiang Huang, Libin Hu, Yufeng Zhang, Jie Guo, Haiping Lu, Yuanhong Hong, Zeqi Zhang, Jingyue Luo, Hui Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Electric Power Research Institute China Southern Power Grid State Key Laboratory of Hvdc Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China Electric Power Research Institute China Southern Power Grid Csg Key Laboratory for Power System Simulation Guangzhou China

ISBN: (纸本)9798331518066

With the increasing scale and complexity of power system, the current scheduling strategy methods often have the problems of limited adaptability and unsatisfactory results. Therefore, a power system scheduling method using DQN (Deep Q Network) is introduced in this study. This method uses neural network model to learn and optimize scheduling strategy, and has strong adaptability and enhanced global optimization ability. Comparative evaluations with genetic algorithms and particle swarm algorithms on IEEE-118 and SG-126 datasets show that the proposed method has significant advantages in terms of cumulative reward and stable runtime. © 2024 IEEE.

关键词： Deep reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

The Development of Simulation Mirror System and its Application in HVDC 6

The Development of Simulation Mirror System and its Applicat...

引用

6th International Conference on HVDC, HVDC 2024

作者： Ou, Kaijian Wu, Wencong Cai, Haiqing State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid National Energy Power Grid Technology R&d Centre. Guangzhou China Guangdong Prov. Key Laboratory of Intelligent Operation and Control for New Energy Power System. Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798350387759

The testing of HVDC new control function and training of HVDC operation personnel require the integration of power system simulation and dispatching automation. This paper presents and builds AC&DC power system simulation mirror system, which is the closed-loop of real-time digital simulation and dispatching automation. Moreover, the applications of simulation mirror system on HVDC new control function testing and HVDC simulation training cabinet are also introduced in this paper. © 2024 IEEE.

关键词： automatic generation control (A GC) automatic HVDC control (ADC) dispatching automation high voltage direct curren t(HVD C) real-time digital simulator(RTDS)

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of power System Simulation Platform Based on Cloud Computing 4

Design and Implementation of Power System Simulation Platfor...

引用

4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Lu, Yuanhong Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue National Energy Power Grid Technology R&d Centre Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

power system simulation involves simulating the operating states and dynamic processes of power systems using mathematical models and computer technology to analyze and predict system behavior. Traditional simulation methods struggle to meet the needs of large-scale, complex power grids due to significant demands for computational resources. Cloud computing, with its high scalability, resource pooling, and on-demand services, is ideally suited for the extensive simulation calculations required by modern power systems. Despite its advantages, the application of cloud computing in power system simulation poses challenges related to data security and privacy, particularly in collaborative simulations involving multiple power entities. This paper presents the design and implementation of a cloud computing-based power system simulation platform. The proposed platform leverages the distributed computing capabilities of cloud computing, integrates federated learning to ensure data privacy, and utilizes advanced power system simulation technologies. This approach significantly enhances simulation efficiency and accuracy while providing data security and privacy protection. The platform offers reliable decision support for power system planning, operation, and control. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Proof-of- Reliability Consensus Mechanism of Blockchain for Remote operation of Relay protection 7

Proof-of- Reliability Consensus Mechanism of Blockchain for ...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Zhang, Yongjie Cong, Wei Tang, Yi Li, Yudun Li, Congcong Shi, Fangfang Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Jinan China State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9798350359558

Remote operation of protective setting value can enhance the adaptability of protection to real-time operating conditions. As centralized solutions, existing models face information security risks. The decentralized nature of blockchain is advantageous for addressing reliability issues in remote operations. This paper treats the control center and protection relay system sub-stations as nodes in blockchain, proposing a Proof-of- Reliability (PoRel) consensus mechanism based on node reliability. The paper first defines sub-station node types and classification criteria, achieving optimal sub-station node grouping based on 0-1 integer programming. Next, it provides a method for evaluating sub-station node reliability and outlines the consensus mechanism process. Finally, using the IEEE 39-bus system as an example, a blockchain system with 27 sub-station nodes is built using Python software, and the proposed consensus mechanism is simulated and verified. The results indicate that the proposed consensus mechanism improves efficiency, achieves decentralization, and helps ensure reliable relay setting remote operations. © 2024 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Distributed power System Simulation and Monitoring Supported by Cloud Technology : A grid optimization scheduling method based on MADRL 4

Distributed Power System Simulation and Monitoring Supported...

引用

4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Luo, Hui Zhang, Jie Hu, Yufeng Zhang, Jingyue Guo, Haiping Huang, Libin Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou China National Energy Power Grid Technology R&d Centre Guangzhou China

ISBN: (纸本)9798331518066

This study explores the optimization of Distributed Generation Systems (DGS) within a Multi-Agent Deep Reinforcement Learning (MADRL) framework. The focus is on transforming the overall optimization goal of the system into a global reward function and designing local rewards adapted to heterogeneous agents. Using the IEEE 33 node system as a case study, the impact of power aggregation and load management from photovoltaic systems, energy storage systems, electric vehicle charging facilities, and HVAC systems is analyzed. The results demonstrate that the proposed edge-cloud collaborative framework optimizes resource allocation and scheduling. The cloud platform is capable of integrating data from diverse sources and managing it in a unified manner, thereby coordinating the operations of multiple heterogeneous agents. The scalability and flexibility of cloud technology allow for the dynamic adjustment of computational resources according to demand, ensuring global optimization and the judicious allocation of resources. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

DC Line Traveling Wave protection Method Based on Improved Hausdorff Distance Comparison 7

DC Line Traveling Wave Protection Method Based on Improved H...

引用

7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Diao, Chengyan Cong, Wei Lin, Xiang Shao, Xiaofei Li, Yudun Li, Congcong Intelligent Dispatch and Control Shandong University Key Laboratory of Power System Jinan China Power Company State Grid Shandong Electric Jinan China

ISBN: (纸本)9798350359558

To improve the reliability of conventional protection using fault travelling wave, this paper proposes a line traveling wave protection method based on improved Hausdorff distance comparison. The traveling waves at the two sides of the line have different propagation laws and change characteristics during external fault and internal fault. First, the traveling waveform is processed by time matching and segmentation calculation. Second, splitting the long waveform into short waveforms and averaging the waveforms at both sides of the block processing, thus calculating the average Hausdorff distance. Last, using PSCAD building a bipolar DC transmission system for simulation verification, the results show that the improved method has high reliability. © 2024 IEEE.

关键词： Electric power system protection

来源：评论

学校读者我要写书评

暂无评论

A Forecast Error Correction Method Based on Seq2Seq and Auto Encoder for Short-term Wind power Forecast Enhancement

A Forecast Error Correction Method Based on Seq2Seq and Auto...

引用

2024 IEEE IAS Industrial and Commercial power System Asia, I and CPS Asia 2024

作者： Ma, Haoyu Yang, Ming Han, Zifen Wang, Bo Che, Jianfeng Li, Menglin Control Shandong University Key Laboratory of Power System Intelligent Dispatch Jinan China State Grid Gansu Power Grid Corporation Lanzhou China China Electric Power Research Institute National Key Laboratory of Renewable Energy Grid-Integration Beijing China

ISBN: (纸本)9798350352290

Accurate wind power forecast (WPF) is highly significant for the safe and economic operation of power systems, and implementing error correction is a common and effective manner to further enhance the forecasting performance. Nonetheless, existing error correction methods fail to consider temporal relationship and may inadvertently increase the error under certain circumstances, thereby compromising the local precision of forecast. In order to address these issues, this paper proposes an error correction model based on Seq2Seq and auto encoder (AE) to improve forecasting accuracy. Firstly, the seq2seq with attention mechanism is introduced to infer the probable error sequence through learning the temporal relationship between the numerical weather prediction sequence and the actual error sequence. Secondly, the samples that the seq2seq providing a positive error inference are collected and the AE model is employed to extract the meteorological features at these moments. Finally, on the basis of the above two models, the error correction method is constructed consisting of preliminary error inference and posterior correction determination. Using operational data from Ningxia province, the proposed method is validated for its remarkable superiority based on the evaluation metrics. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

Research on the Application of Big Data in Transient Stability Simulation of power Systems 4

Research on the Application of Big Data in Transient Stabili...

引用

4th International Conference on Energy, power and Electrical Engineering, EPEE 2024

作者： Zhang, Jie Guo, Haiping Zhao, Yanjun Huang, Libin Hu, Binjiang Luo, Hui Zhang, Jingyue State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou510663 China National Energy Power Grid Technology R&d Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China Csg Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou5106633 China

ISBN: (纸本)9798331518066

With the rapid expansion of smart grids, there is a growing complexity in power system dynamics, which increases the availability of real-time operational data and underscores the need for advanced transient stability simulations. Traditional transient stability analysis methods are constrained by their computational demands and may compromise accuracy. Furthermore, deep learning-based methods often focus on the application of a single algorithm, lacking a systematic consideration of the entire simulation process. To address these challenges, this study proposes a novel approach that leverages big data to enhance transient stability simulations. By integrating deep learning and ensemble learning techniques, the model significantly improves the accuracy and efficiency of predicting transient instabilities. The methodology utilizes a multi-source data acquisition and fusion scheme to ensure data comprehensiveness and a distributed storage architecture to handle large-scale datasets effectively. Advanced visualization technologies are also employed to enhance the interpretability of the simulation results. This comprehensive framework aims to support the safe and stable operation of power systems amid increasing complexity and data availability. © 2024 IEEE.

关键词： Transient stability

来源：评论

学校读者我要写书评

暂无评论

Adaptive Identification of power System operational states Based on Spatio-Temporal Dynamic Graph Neural Networks 4

Adaptive Identification of Power System Operational States B...

引用

4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Zhang, Qingqing Ma, Qinfeng Liu, Mingshun Zhang, Jie Zhu, Yihua Liang, Zhuohang An, Su Pu, Qingxin Dai, Jiang Power Dispatching and Control Center of Guizhou Power Grid Co. Ltd. Guizhou China China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute Guangzhou510663 China National Energy Power Grid Technology R&D Centre Guangzhou510663 China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou510663 China

ISBN: (纸本)9798350361643

The state identification of the operational status of power systems plays a crucial role in ensuring the safety, efficiency, and reliable operation of power systems, forming the foundation for the management and control of power system operations. In the practical operation of power systems, the duration of fault states is short, while the system remains in a safe and stable state for a significantly longer period, leading to an imbalance in the training samples available. This imbalance can cause traditional machine learning classification algorithms to favor the majority categories of power system operational states, resulting in inaccurate evaluation metrics and reduced model generalization capability. To address this issue, this paper introduces a spatiotemporal dynamic graph neural network model for the identification of power system operational states. By incorporating class cost coefficients, focal loss, and cross-entropy in a joint loss function, the model adaptively focuses on the less represented operational state categories during training, improving classification accuracy on imbalanced and challenging samples. Moreover, the integration of temporal information and spatial topology information within the graph neural network enhances the model's robustness and generalization ability in state identification. Experimental results on the standard IEEE 68-bus system demonstrate that, compared to traditional machine learning methods, the proposed joint loss with the spatiotemporal dynamic graph neural network model achieves higher accuracy in the identification of power system operational states, significantly improving classification performance on imbalanced samples with a 4.8% increase in F1-score, reaching a peak F1-score of 98.6%. © 2024 IEEE.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

A Method for power Cable Space Charge Detection Based on Electric Field Stimulation

A Method for Power Cable Space Charge Detection Based on Ele...

引用

2023 Panda Forum on power and Energy, PandaFPE 2023

作者： Pan, Shaoming Zhang, Yiyi Tang, Jie Feng, Bo Zhang, Lei Zhao, Jian Zhang, Longfei Guangxi Power Grid Co. LTD. Electric Power Research Institute Guangxi Key Laboratory of Intelligent Control and Operation of Electric Power Equipment Nanning China

ISBN: (纸本)9798350321173

Aging in XLPE will likely lead to space charge accumulation in it. In this paper, based on polarization and depolarization currents (PDC), a trapped charge measurement method is proposed, which can measure the accumulated space charge in XLPE. First, the de-trap current of trapped charge was extracted by subtracting the stable values of depolarization current and conduction current from the polarization current. Then, the idt· t∼ln(t) curve can be obtained from calculated parameters, which characterizes the density and depth of trapped charge. Finally, the method proposed in this paper were validated by changing different experimental conditions. © 2023 IEEE.

关键词： Electric space charge

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共57页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：