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

162 篇 会议
49 篇 期刊文献

馆藏范围

211 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

100 篇 工学
- 46 篇 电气工程
- 45 篇 计算机科学与技术...
- 34 篇 控制科学与工程
- 34 篇 软件工程
- 21 篇 电子科学与技术（可...
- 20 篇 动力工程及工程热...
- 16 篇 机械工程
- 12 篇 交通运输工程
- 10 篇 信息与通信工程
- 9 篇 石油与天然气工程
- 9 篇 安全科学与工程
- 8 篇 船舶与海洋工程
- 6 篇 仪器科学与技术
- 6 篇 土木工程
- 6 篇 环境科学与工程（可...
- 5 篇 材料科学与工程（可...
- 5 篇 化学工程与技术
- 3 篇 力学（可授工学、理...
- 3 篇 光学工程
- 3 篇 生物工程
44 篇 理学
- 16 篇 物理学
- 14 篇 数学
- 6 篇 化学
- 6 篇 海洋科学
- 4 篇 生物学
- 4 篇 系统科学
- 3 篇 统计学（可授理学、...
26 篇 管理学
- 22 篇 管理科学与工程(可...
- 4 篇 图书情报与档案管...
3 篇 经济学
- 3 篇 应用经济学
2 篇 医学
1 篇 法学
1 篇 文学

主题

14 篇 accuracy
12 篇 power system sta...
10 篇 analytical model...
9 篇 real-time system...
8 篇 simulation
8 篇 stability analys...
8 篇 control systems
7 篇 transient analys...
7 篇 power grids
6 篇 power systems
6 篇 computational mo...
6 篇 feature extracti...
5 篇 wind turbines
5 篇 grid forming
5 篇 switches
5 篇 dispatching
5 篇 wind farms
5 篇 frequency contro...
5 篇 synchronous gene...
5 篇 power system dyn...

机构

42 篇 national energy ...
40 篇 guangdong provin...
27 篇 state key labora...
24 篇 csg key laborato...
10 篇 national energy ...
9 篇 guangdong provin...
7 篇 electric power r...
6 篇 china southern p...
6 篇 state key labora...
5 篇 state key labora...
5 篇 system r&d cente...
5 篇 system r&d cente...
4 篇 optoelectronics ...
4 篇 csg key laborato...
4 篇 information tech...
4 篇 yunnan electric ...
4 篇 school of electr...
4 篇 china southern p...
3 篇 csg key laborato...
3 篇 guangdong provin...

作者

22 篇 yihua zhu
16 篇 zhu yihua
14 篇 huang libin
13 篇 libin huang
11 篇 haiping guo
10 篇 chao luo
10 篇 yuanhong lu
10 篇 jingyue zhang
10 篇 liang tu
9 篇 guo haiping
8 篇 luo chao
8 篇 jie zhang
8 篇 cai haiqing
7 篇 yufeng hu
7 篇 hui luo
7 篇 zhang jingyue
7 篇 zhang jie
7 篇 guanming zeng
7 篇 qi guo
6 篇 jiawei yu

语言

198 篇 英文
11 篇 其他
3 篇 中文

检索条件"机构=Intelligent Control System R&D center"

共 211 条记录，以下是21-30 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：

Technical research on Simulation and Predictive Analysis of Wind Farms in Power systems 4

Technical Research on Simulation and Predictive Analysis of ...

引用

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

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

ISBN: (纸本)9798331518066

To address the issue of insufficient accuracy in wind power output forecasting, this paper proposes a Support Vector Machine (VSM) model based on similar day matching and kernel entropy component analysis for wind power prediction. The model effectively extracts similar day features through the similar day approach and optimizes feature representation by combining it with kernel entropy component analysis (KECA), ultimately employing the SVM model for wind farm power forecasting. Simulation results demonstrate that using this model for optimizing wind power generation predictions enhances the accuracy of wind power forecasts, surpassing the precision of single-method predictions. The Similar day-KECA-SVM method leverages historical data from similar days and features extracted through KECA, with SVM performing the final prediction. This approach adeptly handles complex patterns and nonlinear relationships in wind power forecasting, thereby achieving superior predictive performance. © 2024 IEEE.

关键词： Wind forecasting

来源：评论

学校读者我要写书评

暂无评论

A Novel Energy-Type SVG with Grid Forming control for Grid Voltage and Inertial Support 14

A Novel Energy-Type SVG with Grid Forming Control for Grid V...

引用

14th IEEE International Symposium on Power Electronics for distributed Generation systems, PEdG 2023

作者： Zhang, Jiawei Zhang, Chen Shi, Xianqiang Huang, Lei Li, Xiaobing Cai, Xu Shanghai Jiao Tong Unviersity Key Laboratory of Control of Power Transmission and Conversion Shanghai China Sieyuan Electric Co. Ltd Power System Analysis Department Shanghai China R&d Center Sieyuan Electric Co. Ltd Shanghai China

ISBN: (纸本)9798350328233

Large scale renewable power plants (e.g., wind farms) are typically equipped with the cascaded H-bridge based Static Var Generator (SVG) for voltage regulation of the Point of Common Coupling (PCC). To enhance its capability in gridfriendly integration and operation of renewable power plants, this paper proposed an energy-type Static-var-generator (ESVG) with grid forming (GFM) control characteristics. First, the energy type SVG's converter typology is presented, then a new type of grid forming control utilizing the distributed energy of SVG's dc capacitor is proposed, having two main characteristics: 1) it achieves autonomous frequency response and grid voltage support similar to the majority of GFM controls;2) it has the self dc-voltage balancing capability, which does not require additional energy management control. The state-space model is also built to assess the small-signal stability of the ESVG system. Finally, an electromagnetic transient (EMT) simulation model of a typical grid-tied ESVG system is constructed in Matlab/Simulink, and simulation results show that the proposed control methods and associated characteristics are valid. © 2023 IEEE.

关键词： Frequency response

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

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

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

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

Large-scale non-real-time Simulation Platform for Power systems Based on Cloud Computing 4

Large-scale non-real-time Simulation Platform for Power Syst...

引用

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

作者： Zhang, Jingyue Lu, Yuanhong Hu, Yufeng Zhang, Jie Guo, Haiping Huang, Libin 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 China Southern Power Grid Co. Ltd Guangzhou China State Key Laboratory of Hvdc Electric Power Research Institute China Southern Power Grid Guangzhou China

ISBN: (纸本)9798331518066

The rapid evolution of smart grids has increased the complexity of power system structures and operational characteristics, presenting new challenges in system management and reliability. Traditional power system simulation tools struggle with non-real-time data processing and scalability due to limitations in their architectural designs. Moreover, existing cloud simulation platforms are constrained by closed hardware architectures and lack sufficient non-real-time capabilities. As a solution, we propose a novel large-scale non-real-time simulation platform for power systems based on cloud computing, designed with a modular architecture. By leveraging advanced technologies in cloud computing, parallel computing, and non-real-time simulation, our platform addresses critical needs for scalability, efficiency, and cost-effectiveness. We discuss how integrating these technologies enhances computational performance and supports complex simulation tasks, ultimately improving decision-making and operational efficiency in power system management. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

rddepth: A Lightweight Algorithm for Monocular depth Estimation

RDDepth: A Lightweight Algorithm for Monocular Depth Estimat...

引用

Computer, control and robotics (ICCCr), International Conference on

作者： Gang Xiong Juntong Qi Yan Peng Yuan Ping Chong Wu Institute of Artificial Intelligence Shanghai University Shanghai China R&D center EFY Intelligent Control (Hainan) Technology Co. ltd Hainan China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Current monocular depth estimation algorithms have complex structures and high computational costs, making it difficult to deploy on embedded devices. To address these issues, this paper proposes a lightweight monocular depth estimation algorithm: rddepth, which aims to achieve fast and accurate depth estimation on embedded devices. Firstly, regNet is adopted as the Backbone to balance the accuracy and computational cost of the algorithm; Secondly, denseASPP is used to replace ASPP in the Neck to enhance the accuracy of depth estimation; Thirdly, channel pruning is applied to the Neck and Head of the algorithm to reduce the parameters and computational complexity; Finally, to improve the accuracy of depth estimation, an improved loss function is proposed. Experiments are conducted on the public dataset KITTI, and the algorithm is deployed on the NVIdIA Jetson Xavier NX. Experimental results show that, compared to the baseline, rddepth increases the FPS with FP16 accuracy by 4.34 times to 44.01 FPS, reduces the parameters by 4.7 times, and lowers the GFLOPs by 8.17 times, while also reducing the root mean square error (rMSE) by 0.34. In all, rddepth achieves significant improvements in all aspects and can perform realtime and accurate monocular depth estimation on embedded devices.

关键词： Performance evaluation digital control Accuracy Estimation Channel estimation Neck Computational efficiency

来源：评论

学校读者我要写书评

暂无评论

FTM Implementation and ranging Performance Characterization for Indoor Localization

FTM Implementation and Ranging Performance Characterization ...

引用

IEEE Global Conference on Consumer Electronics (GCCE)

作者： rei Hirata Atsushi Koizumi Hiroshi Oguma Naotaka Sakaguchi Takenori Sumi Yukimasa Nagai Department of Control Information System Enginering NIT Toyama College Toyama Japan Information Technology R&D Center Mitsubishi Electric Corporation Kanagawa Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Indoor positioning technologies for IoT sensing are attracting attention. We are currently exploring standard indoor positioning methods using IEEE 802.11 / Wi-Fi. In this paper, we utilize the IEEE802.11-2016 FTM (Fine Timing Measurement) protocol, which enables to calculate distances between devices and access points (APs). The ranging accuracy of FTM, however, varies depending on multipath propagation of wireless signals and device-specific factors. Therefore, we implemented FTM on two models, Google Pixel4 and Zenfone9, and conducted experiment studies in the gymnasium and corridor of our school to evaluate position detection accuracy. In the gymnasium, the average error was 1.42 m for Zenfone9, while 1.07 m for Pixel4. In the corridor, the average error was 0.66m for Zenfone9, whereas 2.47 m for Pixel4.

关键词： Wireless communication Location awareness Wireless sensor networks Accuracy Protocols distance measurement Sensors Timing Wireless fidelity Standards

来源：评论

学校读者我要写书评

暂无评论

Fault Condition Testing of Photovoltaic Inverter controller Based on Active Support Technology using Hardware-In-the-Loop in rTdS 1

Fault Condition Testing of Photovoltaic Inverter Controller ...

引用

2024 1st International Conference on Smart Grids and Power systems, ICSGPS 2024

作者： Li, Shengnan Hu, Yun He, Xin Zhu, Yihua He, Tingyi Tu, Liang deng, Can Luo, Chao Su, Mingzhang Zeng, Guanming Yunnan Electric Power Research Institute Kunming China National Energy Power Grid Technology R&D Centre Guangzhou China State Key Laboratory of HVDC Electric Power Research Institute CSG 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

Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the rTdS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller. © 2024 Published under licence by IOP Publishing Ltd.

关键词： controllers

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22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：