检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Yang, Qiuling Sadeghi, Alireza Wang, Gang Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Renewable energy sources, elastic loads, and purposeful manipulation of meter readings challenge the monitoring and control of today’s power systems (PS). In this context, fast and robust state estimation (SE) is timely and of major importance to maintain a comprehensive view of the system in real time. Conventional PSSE solvers typically entail minimizing a nonlinear and nonconvex least-squares cost using e.g., the Gauss-Newton method. Those iterative solvers however, are sensitive to initialization, and may converge to local minima. To overcome these hurdles, the present paper adapts and leverages recent advances on image denoising to introduce a PSSE approach with a regularizer capturing a deep neural network (DNN) prior. For the resultant regularized PSSE objective, a "Gauss-Newton-type" alternating minimization solver is developed first. To accommodate real-time monitoring, a novel end-to-end DNN is constructed subsequently by unrolling the proposed alternating minimization solver. The deep PSSE architecture can further account for the power network topology through a graph neural network (GNN) based prior. To further endow the physics-based DNN with robustness against bad data, an adversarial DNN training method is put forth. Numerical tests using real load data on the IEEE 118-bus benchmark system showcase the improved estimation and robustness performance of the proposed scheme compared with several state-of-the-art alternatives. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Capacity Optimization of a Hybrid Energy Storage system Considering Wind-Solar Reliability Evaluation Based on a Novel Multi-Strategy Snake Optimization Algorithm

SSRN

引用

SSRN 2022年

作者： Wang, Chen Jiao, Shangbin Li, Yujun Zhang, Qing School of Automation and Information Engineering Xi’an University of Technology Xi’an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an710048 China

When the capacity configuration of a hybrid energy storage system (HESS) is optimized considering the reliability of a wind turbine and photovoltaic generator (PVG), the sequential Monte Carlo method is typically adopted to simulate the normal operation and fault probability of wind turbines and PVG units. The capacity optimization configuration model of the HESS is constructed with the life cost cycle (LCC) as an objective and the loss of power supply probability (LPSP) as constraints. The variational mode decomposition is used to decompose the unbalanced power in the wind-PV microgrid. The maximum power of the optimized the battery bank and the supercapacitor bank are used to determine the frequency division point, and the high-frequency power and low-frequency power are respectively allocated to the supercapacitor bank and the battery bank. When the snake optimizer (SO) is used to solve the nonconvex capacity optimization configuration model of the HESS, poor accuracy and stability are significant problems. The opposition-based learning, adaptive weight factor, and Lévy flight strategy are introduced in the algorithm framework. Twenty-three test functions show that multi strategy snake optimizer (MSO) significantly improves the accuracy and convergence speed compared with SO. Nine optimization algorithms including MSO are used to solve the capacity optimization problem of the HESS. The results show that MSO is superior to other algorithms in performance and numerical accuracy. It can reduce the LCC 4.5% and ensure that the capacity of the HESS meets the load demand when the wind turbine and PVGs are in a fault state. © 2022, The Authors. All rights reserved.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Stability Analysis for High Frequency Networked control systems

引用

IEEE Transactions on Automatic control 2012年第10期57卷 2694-2700页

作者： Hongjiu Yang Yuanqing Xia Peng Shi Mengyin Fu Institute of Electrical Engineering Yanshan University Qinhuangdao China School of Automation Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing China School of Engineering and Science Victoria University Melbourne VIC Australia Department of Computing and Mathematical Sciences University of Glamorgan Pontypridd UK

This note generalizes the stability analysis for a high frequency networked control system. The high-frequency networked control system is described by a delta operator system with a high frequency constraint. Stability conditions are given for the high frequency delta operator system. Furthermore, by developing the generalized Kalman-Yakubovic-Popov lemma, improved stability conditions are also presented in terms of linear matrix inequalities. Some experiment results are presented to illustrate the effectiveness of the developed techniques.

关键词： Stability analysis Delay Linear matrix inequalities Frequency control Educational institutions Power system stability Numerical stability

来源：评论

学校读者我要写书评

暂无评论

Path Planning and Evaluation for Obstacle Avoidance of Manipulator Based on Improved Artificial Potential Field and Danger Field

Path Planning and Evaluation for Obstacle Avoidance of Manip...

引用

第33届中国控制与决策会议

作者： Jiangbo Zhao Qiang Zhao Junzheng Wang Xin Zhang Yanlong Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology Technical center Xinxing Cathay International Group Co.Ltd. Technology Center of Special Vehicle Manufacture and Evaluation Enginneering of Beijing Beijing North Vehicle Group Co.Ltd.

This paper takes 6-DOF manipulator as the research object,proposes the improved Artificial Potential Field(APF) method to plan the obstacle avoidance path of the manipulator,and combines the Danger Field(DF) method to evaluate the safety of the path planned using *** to the characteristics of the manipulator,the kinematics model of the manipulator is analyzed,and the ball envelope algorithm is applied to simplify the physical model of the *** with the traditional APF method,the improved APF searches in the joint space and introduces the joint attraction potential to improve the search speed and *** problem of local minimum is dealt with by the combination of adding virtual obstacles and increasing joint attraction *** improved APF not only has the advantages of good real-time performance and smooth path,but also solves the problem of the traditional APF falling into a local minimum,and combines the danger field method to judge the path rationality to ensure no collision with *** simulation verification,the proposed method realizes the obstacle avoidance path planning of the manipulator and the safety evaluation of the planned path.

关键词： Artificial potential field Danger field Obstacle avoidance Path planning Collision detection

来源：评论

学校读者我要写书评

暂无评论

Resilient control under Quantization and Denial-of-Service: Co-designing a Deadbeat controller and Transmission Protocol

arXiv

引用

arXiv 2021年

作者： Liu, Wenjie Sun, Jian Wang, Gang Bullo, Francesco Chen, Jie State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China State Key Lab of Intelligent Control and Decision of Complex Systems The School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Department of Control Science and Engineering Tongji University Shanghai201804 China Mechanical Engineering Department The Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States

This paper is concerned with the problem of stabilizing continuous-time linear time-invariant systems subject to quantization and Denial-of-Service (DoS) attacks. In this context, two DoS-induced challenges emerge with the design of resilient encoding schemes, namely, the coupling between encoding strategies of different signals, and the synchronization between the encoder and decoder. To address these challenges, a novel structure that is equipped with a deadbeat controller as well as a delicate transmission protocol for the input and output channels, co-designed leveraging the controllability index, is put forward. When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals. This property is further corroborated by designing encoding schemes as well as conditions that ensure exponential stability of the closed-loop system. On the other hand, when only the output channel is subject to network phenomenon, the proposed structure can achieve exponential stabilization without acknowledgment (ACK) signals, in contrast to existing ACK-based results. Finally, a numerical example is given to demonstrate the practical merits of the proposed approach as well as the theory. © 2021, CC BY.

关键词： Denial-of-service attack

来源：评论

学校读者我要写书评

暂无评论

A statistical learning approach to reactive power control in distribution systems

arXiv

引用

arXiv 2019年

Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a non-convex optimization problem based on the exact model of distribution flow. However, lack of high-precision instrumentation and reliable communications, as well as the heavy computational burden of non-convex optimization solvers render computing and implementing the optimal control challenging in practice. Taking a statistical learning viewpoint, the input-output relationship between each grid state and the corresponding optimal reactive power control is parameterized in the present work by a deep neural network, whose unknown weights are learned offline by minimizing the power loss over a number of historical and simulated training pairs. In the inference phase, one just feeds the real-time state vector into the learned neural network to obtain the 'optimal' reactive power control with only several matrix-vector multiplications. The merits of this novel statistical learning approach are computational efficiency as well as robustness to random input perturbations. Numerical tests on a 47-bus distribution network using real data corroborate these practical merits. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Quantized controller of Linear systems with Disturbances under DoS Attacks

A Quantized Controller of Linear Systems with Disturbances u...

引用

International Conference on control and automation (ICCA)

作者： Wenjie Liu Jian Sun Jie Chen State Key Lab of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Tongji University Shanghai China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

This paper considers the problem of stabilizing linear time-invariant systems with quantized state measurements under both external disturbances and Denial-of-Service (DoS) attacks. An element-based impulsive controller that can stabilize systems when quantization, DoS attacks and bounded disturbances coexist is proposed. A sufficient condition for stability characterized by the parameters of DoS attacks, bit-rate and the quantizer's saturation time is obtained. Moreover, analysis shows that under an appropriate design of parameters of the controller, the resultant saturation time always remains small. A numerical example is given to confirm the validity of the proposed method.

关键词： Denial-of-service attack Quantization (signal) Encoding Decoding Bandwidth Switched systems Eigenvalues and eigenfunctions

来源：评论

学校读者我要写书评

暂无评论

Two-timescale voltage control in distribution grids using deep reinforcement learning

arXiv

引用

arXiv 2019年

作者： Yang, Qiuling Wang, Gang Sadeghi, Alireza Giannakis, Georgios B. Sun, Jian State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN55455 United States

Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it;whereas, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47-bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme. Copyright © 2019, The Authors. 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

Parallel cognition:hybrid intelligence for human-machine interaction and management

引用

Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1765-1779页

作者： Peijun YE Xiao WANG Wenbo ZHENG Qinglai WEI Fei-Yue WANG State Key Laboratory for Management and Control of Complea Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China QingdaoAcademy of Intelligent Industries Qingdao 266109China School of Software Engineering Xi'an Jiaotong UniversityXi'an 710049China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China

As an interdisciplinary research approach,traditional cognitive science adopts mainly the experiment,induction,modeling,and validation *** models are sometimes not applicable in cyber-physical-socialsystems (CPSSs),where the large number of human users involves severe heterogeneity and *** reduce the decision-making conflicts between people and machines in human-centered systems,we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages:descriptive cognition based on artificial cognitive systems (ACSs),predictive cognition with computational deliberation experiments,and prescriptive cognition via parallel behavioral *** make iteration of these stages constantly on-line,a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual’s cognitive *** experiments on two representative scenarios,urban travel behavioral prescription and cognitive visual reasoning,indicate that our parallel cognition learning is effective and feasible for human behavioral prescription,and can thus facilitate human-machine cooperation in both complex engineering and social systems.

关键词： Cognitive learning Artificial intelligence Behavioral prescription

来源：评论

学校读者我要写书评

暂无评论

Exact Computation of Delay Margin by PID control: It Suffices to Solve a Unimodal Problem!

Exact Computation of Delay Margin by PID Control: It Suffice...

引用

第三十八届中国控制会议

作者： Jianqi Chen Dan Ma Yong Xu Jie Chen Department of Electronic Engineering City University of Hong Kong College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University The School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology

In this paper we study delay robustness of PID controllers in stabilizing systems containing uncertain, variable *** consider second-order unstable systems and seek analytical characterization and exact computation of the PID delay margin,where by PID delay margin we mean the maximal range of delay values within which the system can be robustly stabilized by a PID controller. Our contribution is threefold. First, we show that the delay margin achieved by PID control coincides with that by PD controllers. Second, we show that other than helping stabilize the delay-free part of a plant, the proportional control contributes no action to increase the delay margin. Finally, we show that the PID delay margin can be computed efficiently by solving a unimodal problem, that is, a univariate optimization problem that admits a unique maximum and hence is a convex optimization problem in one variable. This unimodal problem is one of pseudo-concave optimization and hence can be solved using standard convex optimization or gradient-based methods. As such, from a computational perspective, the PID delay margin problem is completely resolved in this paper! The results not only insure that the PID delay margin problem be readily solvable, but also provide fundamental conceptual insights into the PID control of delay systems, and analytical justifications to long-held engineering intuitions and heuristics, thus lending useful guidelines in the tuning and analytical design of PID controllers.

关键词： Delay margin robust stabilization PID controller uncertain time delay nonlinear programming pseudo-concavity

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：