检索结果-内蒙古大学图书馆

Dynamic Modeling of Key Operating Parameters for Supercritical Circulating Fluidized Bed Units based on Data-Knowledge-Driven Method

引用

Journal of Thermal Science 2024年第3期33卷 1216-1230页

作者： YU Haoyang GAO Mingming ZHANG Hongfu CHEN Jiyu LIU Jizhen YUE Guangxi The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206China CHN Energy New Energy Technology Research Institute Co. Ltd.Beijing102209China Department of Energy and Power Engineering Tsinghua UniversityBeijing100084China

To address the pressing need for intelligent and efficient control of circulating fluidized bed(CFB)units,it is crucial to develop a dynamic model for the key operating parameters of supercritical circulating fluidized bed(SCFB)***,data-knowledge-driven dynamic model of bed temperature,load,and main steam pressure of the SCFB unit has been ***,a knowledge-driven method is employed to develop a dynamic model for key operating parameters of SCFB *** model parameters are determined based on the operating data of the unit and continuously optimized in real ***,Bidirectional Long Short-Term Memory combined with Convolutional Neural Network and Attention Mechanism is utilized to build the dynamic model of bed temperature,load,and main steam ***,a collaboration and integration method based on the critic weight method and the variation coefficient method is proposed to establish data-knowledge-driven model of key operating parameters for SCFB *** model displays great accuracy and fitting ability compared with other methods and effectively captures the dynamic characteristics,which can provide a research basis for the design of intelligent flexible control mode of SCFB unit.

关键词： circulating fluidized bed dynamic model data-knowledge-driven method deep learning power plant

来源：评论

学校读者我要写书评

暂无评论

Privacy-Preservation for Distributed Quantized k-means Clustering 59

Privacy-Preservation for Distributed Quantized k-means Clust...

引用

59th Annual Allerton Conference on Communication, control, and Computing, Allerton 2023

作者： Rikos, Apostolos I. Manitara, Nicolas E. Oliva, Gabriele Hadjicostis, Christoforos N. Boston University Division of Systems Engineering Department of Electrical and Computer Engineering BostonMA02215 United States University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Università Campus Bio-Medico di Roma Unit of Automatic Control Department of Engineering Rome00128 Italy

ISBN: (纸本)9798350328141

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node's state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph. © 2023 IEEE.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

Distributed Event-Triggered Adaptive Cooperative Circumnavigation control of Networked Unmanned Aerial Vehicles 20

Distributed Event-Triggered Adaptive Cooperative Circumnavig...

引用

20th International Multi-Conference on Systems, Signals and Devices, SSD 2023

作者： Ghommam, Jawhar Fareh, Raouf Al Naimi, Ibrahim Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Oman Laboratory of Control Energy Management ENIS Tunisia University of Sharjah Electrical Engineering Dept. United Arab Emirates

ISBN: (纸本)9798350332568

In this paper, the objective for a group of unmanned aerial vehicle agents (UAVs) to achieve three dimensional circumnavigation around a moving target which information is made available to all agents in the group. The cooperative circumnavigation is to drive the UAVs to orbit around the target according to a given elliptical desired spatial formation. Due to the thrust limitation needed to fly the drone, existing cyclic pursuit algorithms cannot be extended directly to achieve this objective. Thus the proposed algorithm is worked out to take into account this constraint in order to achieve such objective. The drones are subject to unknown external disturbance, also the masses of those agent drones are assumed to be unknown. Furthermore, the communication cost can be decreased and the Zeno behavior is shown to be excluded. The proposed controller guarantees the bounded control effort irrespective of the external disturbance and model uncertainties of the drone. Numerical simulations are conducted to illustrate the efficacy of the approach. © 2023 IEEE.

关键词： Drones

来源：评论

学校读者我要写书评

暂无评论

Zero-phase CARIMA Filtering and Application in Wind-storage System Sizing and Power Dispatch Optimization

引用

CSEE Journal of Power and Energy Systems 2024年第6期10卷 2283-2295页

作者： Wei Wang Peng Chen Guorui Ren Jizhen Liu Fang Fang Zhe Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Department of Energy Technology Aalborg University9220 AalborgDenmark

Hybrid energy storage system(HESS)is an effective way to mitigate wind power fluctuations on multi-time scale,and can improve influence of large-scale grid-connected wind power on stability and reliability of power system.A novel methodology named zero-phase controlled auto-regressive integrated moving-average(CARIMA)filter is proposed to integrate HESS to smooth wind power ***,a design method for zero-phase CARIMA filter is provided,and then used to determine grid-connected power for a wind storage system and size *** reasons,direct current(DC)component caused by energy storage efficiency and grid-connected power delay caused by phase shift,for causing superfluous energy storage configuration are *** addition,a nonlinear programming scheduling strategy considering battery degradation is *** imbalance caused by efficiency difference during dynamic adjustment of energy storage output power is ***,thermostatically controlled loads(TCLs)are integrated in sizing and scheduling HESS to reduce energy storage demand and improve operating conditions of energy ***,effectiveness of the proposed strategy is verified by a case study.

关键词： CARIMA demand response energy storage optimization wind power zero-phase

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison of Kalman Filter and Extended Kalman Filter for Human Tracking and Prediction with Particle Swarm Optimisation 1

引用

Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2022

作者： Ajasa, Abiodun Afis Wahyudi, Nawawi Sophan Department of Control and Mechatronics Engineering School of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Department of Electronics and Computer Engineering Lagos State University Epe Campus Lagos Nigeria

ISBN: (数字)9789811987038

ISBN: (纸本)9789811987021

In the past two decades, object tracking has progressively advanced in computer vision and image processing. Tracking is a collection of algorithms that detect and track objects in a video sequence. This has resulted in a broad variety of applications, including surveillance, biometric identification or biological imaging, human-machine interactions, traffic control, and intelligent vehicles, all of which have benefited from tracking applications. This paper presents an IoT-based human tracking system that employs the Kalman filter (KF) and extended Kalman filter (EKF) algorithms. However, the performance of the filters is impacted by noise. Therefore, instead of manually tuning the KF and EKF’s process noise and measurement error, particle swarm optimisation (PSO) is utilised to optimise them. Four 2-dimensional models (namely conventional KF and EKF, optimised KF-PSO, and EKF-PSO) were developed and evaluated using ten distinct human sets of data containing 100 samples each. The object’s tracked positions are estimated in horizontal and vertical directions. Accuracy analysis was used to compare the four models’ quality performance. With an average mean square error of 5.99 mm (or 0.599%), the EKF-PSO model outperformed the conventional EKF model, which had an error of 7.18 mm (or 0.718%). A 17.2 mm (or 1.72%) error was found in the KF-PSO model. The last one is the traditional KF model, yielding an error of 22.4 mm (or 2.24%). As a result of its higher accuracy, the EKF-PSO model outperforms the other three models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

引用

38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

来源：评论

学校读者我要写书评

暂无评论

Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

引用

Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

来源：评论

学校读者我要写书评

暂无评论

Reference Model Design in Multiple Timescale Adaptive control

Reference Model Design in Multiple Timescale Adaptive Contro...

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale Systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

来源：评论

学校读者我要写书评

暂无评论

Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

引用

2024 International Conference on control, Automation and Diagnosis, ICCAD 2024

作者： Shahid, Kamal Gulzar, Muhammad Majid Raza, Muhammad Ali Habib, Salman Ahmad, Hasnain University of The Punjab Institute of Electrical Electronics and Computer Engineering Lahore Pakistan King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350361025

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings. © 2024 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Hamiltonian learning using machine-learning models trained with continuous measurements

引用

Physical Review Applied 2024年第4期22卷 044080页

作者： Kris Tucker Amit Kiran Rege Conor Smith Claire Monteleoni Tameem Albash Department of Applied Mathematics Department of Computer Science Center for Quantum Information and Control Department of Electrical and Computer Engineering Paris France Center for Computing Research

We build upon recent work on the use of machine-learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning, where the weak-measurement training record can be labeled with known Hamiltonian parameters, and (2) unsupervised learning, where no labels are available. The first has the advantage of not requiring an explicit representation of the quantum state, thus potentially scaling very favorably to a larger number of qubits. The second requires the implementation of a physical model to map the Hamiltonian parameters to a measurement record, which we implement using an integrator of the physical model with a recurrent neural network to provide a model-free correction at every time step to account for small effects not captured by the physical model. We test our construction on a system of two qubits and demonstrate accurate prediction of multiple physical parameters in both the supervised context and the unsupervised context. We demonstrate that the model benefits from larger training sets, establishing that it is “learning,” and we show robustness regarding errors in the assumed physical model by achieving accurate parameter estimation in the presence of unanticipated single-particle relaxation.

关键词： Entanglement detection Nonlocality Quantum parameter estimation Machine learning Weak measurements

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：