检索结果-内蒙古大学图书馆

New Tuning Methods of Both PID and ADRC for MIMO Coupled Nonlinear Uncertain systems ⁎

IFAC-PapersOnLine 2020年第2期53卷 1325-1330页

作者： Sheng Zhong Yi Huang Lei Guo Key Lab. of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences and School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China

This paper proposes new tuning methods for both the famous proportional-integral-derivative (PID) control and the active disturbance rejection control (ADRC) of multi-input multi-output (MIMO) coupled nonlinear uncertain systems. Firstly, a quantitative lower bound to the bandwidth of the parallel extended state observers (ESOs) of ADRC is given, which is not necessarily of high gain. Then, inspired by an inherent but less noticed relationship between PID and ADRC, a new and concrete PID tuning rule is introduced, which can achieve both the strong robust decoupling control and good tracking performance of the MIMO closed-loop systems. Finally, the theoretical results, which reveal that why and how both PID control and ADRC can effectively deal with decoupling problem for MIMO coupled nonlinear uncertain systems, are verified by simulations.

关键词： Multi-input Multi-output (MIMO) Coupled Nonlinear Uncertain systems Proportional-Integral-Derivative (PID) Active Disturbance Rejection control (ADRC) Extended State Observer (ESO) Disturbance Estimation

来源：评论

学校读者我要写书评

暂无评论

Medium and long-term wind energy forecasting method considering multi-scale periodic pattern 10

Medium and long-term wind energy forecasting method consider...

引用

2020 10th International Conference on Power, Energy and Electrical Engineering, CPEEE 2020

作者： Lin, Yisha Lu, Zongxiang Qiao, Ying Li, Mingjie Liang, Zhifeng State Key Lab of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Haidian District Beijing100084 China State Grid Corporation of China Haidian District Beijing100031 China

Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered. © 2020 The Authors, published by EDP Sciences.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

Wind Power Generation Capacity Evaluation Based on Correlation Analysis of Adjacent Wind Farms

Wind Power Generation Capacity Evaluation Based on Correlati...

引用

Sustainable Power and Energy Conference (iSPEC)

作者： Shuxin Sun Ying Qiao Zongxiang Lu Yanhong Ma State Key Lab of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China Wind Power Technology Center State Grid Gansu Electric Power Company Lanzhou China

ISBN: (纸本)9781665414401

Accurate assessment of the power generation capacity of the wind farm is the key basis for incorporating it into power system scheduling and other optimizing operation activities. Traditional evaluation methods, such as benchmarking wind turbine method, have higher requirements for input data, but many wind farms in operation for many years are difficult to meet such data conditions. A new power generation capacity evaluation method based on correlation analysis of adjacent wind farms is proposed in this paper. Meteorological data and power of adjacent wind farms are used as replaced input data based on correlation analysis between the target farm with its adjacent farms. First, anticipate wind speed data and then construct the wind power curve with three different neural networks to evaluate power generation capacity. Afterwards, adjacent wind farms are used as data sources to fit the power data of the target wind farm with the application of influence radius method, inverse square distance method and bilinear interpolation method. Finally, combined with data in the actual case, investigate the advantages and disadvantages of different methods in the evaluation of power generation capacity.

关键词： Interpolation Analytical models Correlation Wind speed Soft sensors Neural networks Wind farms

来源：评论

学校读者我要写书评

暂无评论

Cause Identification of Single-line-to-ground Fault in Distribution System

Cause Identification of Single-line-to-ground Fault in Distr...

引用

International Conference on Power System Technology (POWERCON)

作者： Kun Liu Xuan Ren Bin Wang Key Lab for Protective Relaying Real Time Digital Simulation of Guangdong Power Grid Co. Ltd. Power Dispatch Control Center of Guangdong Power Grid Co. Ltd Guangzhou China Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Tsinghua University Beijing China

ISBN: (纸本)9781665407380

Cause identification of single-line-to-ground fault in the distribution system is of great significance to the electric workers for fault isolation and rapid maintenance. The key of fault cause identification is to accurately and quickly distinguish human electric shock faults from non-biological faults. In this paper, human electric shock faults and non-biological faults are modeled and fault characteristics are analyzed. Based on the differences between shape and rotation characteristic of the volt-ampere characteristic curves, a fault cause identification method which can distinguish human electric shock faults from non-biological faults is proposed. Simulations show the effectiveness of the method.

关键词： Fault diagnosis Analytical models Shape Electric shock Maintenance engineering Power systems

来源：评论

学校读者我要写书评

暂无评论

Is the skip connection provable to reform the neural network loss landscape? 29

Is the skip connection provable to reform the neural network...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Lifu Shen, Bo Zhao, Ning Zhang, Zhiyuan Department of Electronic and Information Engineering Beijing Jiaotong University Beijing China Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China

ISBN: (纸本)9780999241165

The residual network is now one of the most effective structures in deep learning, which utilizes the skip connections to "guarantee" the performance will not get worse. However, the non-convexity of the neural network makes it unclear whether the skip connections do provably improve the learning ability since the nonlinearity may create many local minima. In some previous works [Freeman and Bruna, 2016], it is shown that despite the non-convexity, the loss landscape of the two-layer ReLU network has good properties when the number m of hidden nodes is very large. In this paper, we follow this line to study the topology (sub-level sets) of the loss landscape of deep ReLU neural networks with a skip connection and theoretically prove that the skip connection network inherits the good properties of the two-layer network and skip connections can help to control the connectedness of the sub-level sets, such that any local minima worse than the global minima of some two-layer ReLU network will be very "shallow". The "depth" of these local minima are at most O(m(?-1)/n), where n is the input dimension, ? © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

One-shot face reenactment using appearance adaptive normalization

arXiv

引用

arXiv 2021年

作者： Yao, Guangming Yuan, Yi Shao, Tianjia Li, Shuang Liu, Shanqi Liu, Yong Wang, Mengmeng Zhou, Kun NetEase Fuxi AI Lab. China State Key Lab of CAD&CG Zhejiang University China School of Computer Science and Technology Beijing Institute of Technology China Institute of Cyber-Systems and Control Zhejiang University China

The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. The core of our network is a novel mechanism called appearance adaptive normalization, which can effectively integrate the appearance information from the input image into our face generator by modulating the feature maps of the generator using the learned adaptive parameters. Furthermore, we specially design a local net to reenact the local facial components (i.e., eyes, nose and mouth) first, which is a much easier task for the network to learn and can in turn provide explicit anchors to guide our face generator to learn the global appearance and pose-and-expression. Extensive quantitative and qualitative experiments demonstrate the significant efficacy of our model compared with prior one-shot methods. © 2021, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Ultra-short-term solar power forecasting based on a modified clear sky model

Ultra-short-term solar power forecasting based on a modified...

引用

Chinese control Conference (CCC)

作者： Yuan Ma Xuemin Zhang Shengwei Mei Zhao Zhen Rui Gao Zijie Zhou State Key Lab of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

The ultra-short-term prediction of photovoltaic power system is of great significance to the safe operation of power system. However, the daily and annual periodicity of solar radiation brings strong non-stationary to the photovoltaic power series, which makes it difficult to predict the photovoltaic power series. In order to overcome the problem that the detailed clear sky model relies on a large number of plant parameters, this paper proposes a prediction algorithm based on a modified clear sky model. Firstly, historical data and geographical location of a certain site are utilized to build a modified clear sky model, then the theoretical clear sky power curve fitted by the modified clear sky model are divided by the actual historical power output to get a stabilized time series, finally the method of "online update" is adopted to forecast PV plant power output in the ultra-short-term time horizon (0-4 h). The test results of a photovoltaic power station in Ningxia show that the proposed model can reduce the prediction error of the 4th hour to about 3%.

关键词： Photovoltaic systems Predictive models Data models Mathematical model Forecasting

来源：评论

学校读者我要写书评

暂无评论

Switching Current Imbalance Mitigation for Paralleled SiC MOSFETs Using Common-mode Choke in Gate Loop

Switching Current Imbalance Mitigation for Paralleled SiC MO...

引用

IEEE Energy Conversion Congress and Exposition

作者： Jiye Liu Zedong Zheng State Key Lab of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China

ISBN: (数字)9781728158266

ISBN: (纸本)9781728158273

Paralleling SiC MOSFETs is a popular way to increase the current capacity. However, due to asymmetric layout, device parameter mismatch and other factors, imbalanced switching or dynamic current is observed among the paralleled switches in multi-device paralleling application which results in unequal switching loss, challenging the security and reliability of the system. In this paper, a common-mode choke in the gate loop is proposed and analyzed to suppress the imbalanced switching current induced by asymmetric layout in a half-bridge configuration with a Kelvin-source connection. The mechanism of the proposed method is comprehensively investigated to show that though the common-mode choke is placed in the gate loop, it can also mitigate the imbalanced current caused by asymmetric power loop. And it also shows that only the leakage inductance of the choke is seen in the gate loop and thus the switching speed is not expected to be influenced. The proposed method can be easily extended to multi-device paralleling application and the switching transients is hardly influenced by the common-mode choke. Simulation and experimental results are presented to verify the proposed method.

关键词： Inductance MOSFET Silicon carbide Switches Logic gates Inductors Transient analysis

来源：评论

学校读者我要写书评

暂无评论

Autonomous Decentralized control of Distributed Generation using Multi-Agent Reinforcement Learning

Autonomous Decentralized Control of Distributed Generation u...

引用

IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Jian Liul Weifeng Xul Zhijun Liul Guanhua Ful Yunpeng Jiang Jun Wang Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Co. Ltd. Hangzhou China Wuxi Research Institute Of Applied Technologies Tsinghua University Dept. of Electrical Engineering State Key Lab of Control and Simulation of Power Systems and Generation Equipments Tsinghua University Beijing China

ISBN: (纸本)9798350399677

With the large-scale integration of distributed generations (DGs), a centralized control approach is being challenged concerning communication efficiency, resiliency to communication failure, privacy, and scalability. An autonomous decentralized control for DGs in distribution networks is proposed to ensure privacy while also reducing the computational burden. Meanwhile,a multi-agent reinforcement learning method considering privacy protection constraints is developed to solve the autonomous decentralized control problem. The effectiveness of the proposed control method is verified by the numerical example of the modified 141-bus distribution system test_feeder.

关键词： Privacy Scalability Computational modeling Decentralized control Asia Reinforcement learning Distribution networks

来源：评论

学校读者我要写书评

暂无评论

Accelerating exploitation and integration of global renewable energy

引用

Innovation 2025年

作者： Wang, Jianxiao Chen, Xinjiang Zhuang, Minghao Li, Yan Ruan, Ziwen Wang, Yuhan Zhang, Ning Song, Jie He, Kebin Lu, Xi National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Peking University Ordos Research Institute of Energy Ordos017000 China State Key Laboratory of Nutrient Use and Management College of Resources and Environmental Sciences China Agricultural University Beijing100193 China College of Environmental Science and Engineering Tongji University Shanghai200092 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Key Lab of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China Institute for Carbon Neutrality Tsinghua University Beijing100084 China

来源：评论

学校读者我要写书评

暂无评论

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：

请选择收藏分类：

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

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：