检索结果-内蒙古大学图书馆

On battery state estimation algorithms for electric ship applications

ELECTRIC POWER SYSTEMS RESEARCH 2017年 151卷 115-124页

作者： Misyris, George S. Marinopoulos, Antonios Doukas, Dimitrios I. Tengner, Tomas Labridis, Dimitris P. ABB Corp Res S-72178 Vasteras Sweden Aristotle Univ Thessaloniki Sch Elect & Comp Engn POB 486 Thessaloniki 54124 Greece European Commiss Joint Res Ctr Directorate Energy Transport & Climate C Petten Netherlands

In the last decade increasing concerns about the environment, financial reasons based on fuel prices and application-specific operational challenges have been driving the development of electric propulsion and hybrid or full -electric ships. The use of battery energy storage systems (BESS), which are suitable for a broad range of ship applications with different requirements, can reduce the use of fossil fuels. In this paper the benefits of an onboard DC grid, as applied by ABB, are briefly presented. The integration of BESS and the challenges for ship applications are also discussed. The focus of this paper is on a parameter identification method for an electric model of a battery and the evaluation and validation of a battery state estimation method, in respect to the accuracy requirements for ship applications. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Batteries Electric ships Energy storage Marine electrical equipment state estimation algorithms

来源：评论

学校读者我要写书评

暂无评论

Recursive parameter and state estimation methods for observability canonical state-space models exploiting the hierarchical identification principle

引用

IET CONTROL THEORY AND APPLICATIONS 2019年第16期13卷 2538-2545页

作者： Cui, Ting Ding, Feng Alsaedi, Ahmed Hayat, Tasawar Jiangnan Univ Sch Internet Things Engn Minist Educ Key Lab Adv Proc Control Light Ind Wuxi 214122 Jiangsu Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 266061 Shandong Peoples R China King Abdulaziz Univ Dept Math Jeddah 21589 Saudi Arabia

This study presents two recursive parameter and state estimation algorithms for state-space systems, considering the process noises and observation noises. Based on the Kalman filter and hierarchical identification principle, the authors propose a Kalman filtering-based hierarchical generalised stochastic gradient algorithm to jointly estimate the parameters and states of observability canonical state-space systems. With the aim of achieving more accurate parameter estimation, they present a Kalman filtering-based hierarchical multi-innovation generalised stochastic gradient algorithm by utilising a range of available data and more information at each recursion. Finally, the effectiveness of the proposed algorithms is validated through a numerical simulation example.

关键词： Kalman filters stochastic processes gradient methods state-space methods state estimation recursive estimation observability observability canonical state-space models hierarchical identification principle recursive parameter state estimation algorithms process noises observation noises observability canonical state-space systems recursion parameter estimation numerical simulation Kalman filtering-based hierarchical multiple innovation generalised stochastic gradient algorithm

来源：评论

学校读者我要写书评

暂无评论

state-of-Charge estimation for Li-Ion Batteries: A More Accurate Hybrid Approach

引用

IEEE TRANSACTIONS ON ENERGY CONVERSION 2019年第1期34卷 109-119页

作者： Misyris, George S. Doukas, Dimitrios I. Papadopoulos, Theofilos A. Labridis, Dimitris P. Agelidis, Vassilios G. ABB Corp Res S-72178 Vasteras Sweden Aristotle Univ Thessaloniki Sch Elect & Comp Engn Thessaloniki 54124 Greece Tech Univ Denmark Dept Elect Engn DK-2800 Lyngby Denmark Democritus Univ Thrace Dept Elect & Comp Engn Power Syst Lab Xanthi 67100 Greece

Modeling of battery energy storage systems used for applications, such as electric vehicles and smart grids, emerged as a necessity over the last decade and depends heavily on the accurate estimation of battery states and parameters. Depending on the battery-cell type and operation, a combination of algorithms is used to identify battery parameters and define battery states. This paper deals with robust Li-ion batteries modeling with a specific focus on a hybrid approach for a more accurate state-of-charge (SOC) estimation. The analysis presents a detailed description of the state-of-the-art stand-alone SOC estimation methods and focuses on a hybrid SOC estimation technique to improve accuracy under varying conditions. Emphasis is given on performance improvements of the proposed hybrid approach compared to the conventional methods, whereas a thorough experimental validation is presented to evaluate the accuracy of the proposed method.

关键词： Battery energy storage systems (BESS) capacity estimation Coulomb Counting equivalent circuit model (ECM) model-based methods parameter identification state estimation algorithms state-of-charge (SOC) estimation

来源：评论

学校读者我要写书评

暂无评论

Hybrid Descriptor System state estimation through an IMM Approach

引用

IFAC-PapersOnLine 2015年第28期48卷 579-584页

作者： Zhang, Liangquan Zhang, Qinghua INRIA Campus de Beaulieu 35042 Rennes France

Stochastic hybrid systems have been largely studied in the literature in the framework of Markov mode transitions and linear Gaussian state space mode models. In order to generalize such results to hybrid systems involving phenomena characterized by difierential- algebraic equations, this paper studies the problem of state estimation for stochastic hybrid systems with modes described by descriptor equations. The proposed state estimation algorithm follows the interacting multiple model (IMM for short) approach, but the classical state space system Kalman filters are replaced by descriptor system Kalman filters. Because of the dificulty for computing the innovation of each descriptor system Kalman filter, a new method for likelihood evaluation is proposed, as one important step of the new IMM algorithm. Numerical examples are presented to illustrate the performance of the proposed algorithm. © 2015

关键词： Kalman filters Describing functions Hybrid systems state estimation Stochastic models Stochastic systems Algebraic equations Descriptor equation Descriptor systems IMM estimator Interacting multiple model state estimation algorithms state space systems Stochastic hybrid systems

来源：评论

学校读者我要写书评

暂无评论

Visual Marker based Multi-Sensor Fusion state estimation

引用

IFAC-PapersOnLine 2017年第1期50卷 16003-16008页

作者： Sanchez-Lopez, Jose Luis Arellano-Quintana, Victor Tognon, Marco Campoy, Pascual Franchi, Antonio Madrid Spain ESIME-UA National Polytechnic Institute Mexico City Mexico LAAS-CNRS Université de Toulouse CNRS Toulouse France

This paper presents the description and experimental results of a versatile Visual Marker based Multi-Sensor Fusion state estimation that allows to combine a variable optional number of sensors and positioning algorithms in a loosely-coupling fashion, incorporating visual markers to increase its performances. This technique allows an aerial robot to navigate in different environments and carrying out different missions with the same state estimation architecture, exploiting the best from every sensor. The state estimation algorithm has been successfully tested controlling a quadrotor equipped with an extra IMU and a RGB camera used only to detect visual markers. The entire framework runs on an onboard computer, including the controllers and the proposed state estimator. The whole software is made publicly available to the scientific community through an open source implementation. © 2017

关键词： Visual servoing Air navigation Antennas Image sensors Navigation Open source software Open systems state estimation Autonomous Mobile Robot Inertial sensor Open source implementation Position estimation Positioning algorithms Scientific community Sensor fusion state estimation algorithms

来源：评论

学校读者我要写书评

暂无评论

LoDiM: A Novel Power System state estimation Method with Dynamic Measurement Selection

LoDiM: A Novel Power System State Estimation Method with Dyn...

引用

General Meeting of the IEEE-Power-and-Energy-Society (PES)

作者： Zhang, Jinghe Welch, Greg Bishop, Gary Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

ISBN: (纸本)9781457710018

For decades, state estimation has been a fundamental aspect of power systems. However for large-scale and wide-area interconnected power systems, the required computation makes real-time on-line estimation a major challenge. In this paper we present a new method we call Lower Dimensional Measurement-space (LoDiM) state estimation. LoDiM is based on the Extended Kalman filter-popular because of its efficiency, robustness, and typical accuracy. LoDiM, which can take advantage of modern parallel computation techniques, may be useful for other large-scale, real-time on-line and computationally-intensive state tracking systems beyond the power systems, such as weather forecasting or gas-pipeline state estimation. Although LoDiM is presented in the context of the Kalman filter, the associated measurement selection procedure is not filter-specific, i.e. it can be used with other state estimation methods such as particle and unscented filters. If desired, robust estimation techniques can also be employed to detect and eliminate outlier measurements.

关键词： Power systems state estimation algorithms Dynamic Measurement Selection Power system simulation Kalman Filter Parallel Compuatation

来源：评论

学校读者我要写书评

暂无评论

Assessment of Dynamic Measurement Intervals for state estimation in Future Distribution Systems

Assessment of Dynamic Measurement Intervals for State Estima...

引用

14th IEEE Madrid PowerTech Conference (IEEE POWERTECH)

作者： Sirige, Sindhura Blaauwbroek, Niels Torres, Jose Luis Rueda Palensky, Peter Lekic, Aleksandra Delft Univ Technol Dept Elect Sustainable Energy Delft Netherlands Stedin DSO Grid Strategy Dept Rotterdam Netherlands

ISBN: (纸本)9781665435970

The ongoing energy transition introduces new load and generation units like photovoltaic generation in the medium-voltage systems network. These generation units increase the variance of the power injections, resulting in increased stochasticity of the power flow values. For confident assessment of the system's operating condition, a measurement scheme is essential for accurate state estimation at every minute of the day. This paper presents a performance assessment of the Weighted Least Square (WLS) distribution system state estimation algorithm with respect to the measurement intervals, and proposes a measurement scheme considering the measurement locations and intervals that satisfy a required error limit. The scheme is tested on a synthetic test model of a Dutch MV network, which takes into account projected (year 2040) and highly stochastic power flow profiles. Numerical results show that the proposed algorithm for the measurement scheme determination successfully satisfies the chosen error limit.

关键词： Active distribution systems measurement intervals state estimation algorithms stochastic power flows

来源：评论

学校读者我要写书评

暂无评论

state estimation in Electrical Networks

State Estimation in Electrical Networks

引用

作者： Mosbah, Hossam Dalhousie University

学位级别：master

The continuous growth in power system electric grid by adding new substations lead to construct many new transmission lines, transformers, control devices, and circuit breakers to connect the capacity (generators) to the demand (loads). These components will have a very heavy influence on the performance of the electric grid. The renewable technical solutions for these issues can be found by robust algorithms which can give us a full picture of the current state of the electrical network by monitoring the behavior of phase and voltage magnitude. In this thesis, the major idea is to implement several algorithms including weighted least square, extend kalman filter, and interior point method in three different electrical networks including IEEE 14, 30, and 118 to compare the performance of these algorithms which is represented by the behavior of phases and magnitude voltages as well as minimize the residual of the balance load flow real time measurements to distinguish which one is more robust. Also to have a particular understanding of the comparison between unconstraint and constraint algorithms. Advisors/Committee Members: (external-examiner), *** (Jack) Ilow (graduate-coordinator), Dr. Jason Gu (thesis-reader), Dr. William J. Phillips (thesis-reader), *** Elhawary (thesis-supervisor), Not Applicable (ethics-approval), No (manuscripts), No (copyright-release)

关键词： state estimation Weighted Least Squares Extend KalmanFilter Interior Point Method Optimization state estimation algorithms

来源：评论

学校读者我要写书评

暂无评论

LoDiM: A Novel Power System state estimation Method with Dynamic Measurement Selection

LoDiM: A Novel Power System State Estimation Method with Dyn...

引用

IEEE Power and Energy Society General Meeting

作者： Jinghe Zhang Greg Welch Gary Bishop Department of Computer Science The University of North Carolina at Chapel Hill

ISBN: (纸本)9781457710001

For decades, state estimation has been a fundamental aspect of power systems. However for large-scale and wide-area interconnected power systems, the required computation makes real-time on-line estimation a major challenge. In this paper we present a new method we call Lower Dimensional Measurement-space (LoDiM) state estimation. LoDiM is based on the Extended Kalman filter - popular because of its efficiency, robustness, and typical accuracy. LoDiM, which can take advantage of modern parallel computation techniques, may be useful for other large-scale, real-time on-line and computationally-intensive state tracking systems beyond the power systems, such as weather forecasting or gas-pipeline state estimation. Although LoDiM is presented in the context of the Kalman filter, the associated measurement selection procedure is not filter-specific, i.e. it can be used with other state estimation methods such as particle and unscented filters. If desired, robust estimation techniques can also be employed to detect and eliminate outlier measurements.

关键词： Power systems state estimation algorithms Dynamic Measurement Selection Power system simulation Kalman Filter Parallel Computation power systems PARALLEL PROCESSING (COMPUTERS) Power system simulation Kalman filters state estimation power system state estimation dynamic testing weather forecasting

来源：评论

学校读者我要写书评

暂无评论

Effective recursive parallelotopic bounding for robust output-feedback control

引用

IFAC-PapersOnLine 2018年第15期51卷 1032-1037页

作者： Sharma, Udit Thangavel, Sakthi Gottu Mukkula, Anwesh Reddy Paulen, Radoslav Process Dynamics and Operations Group Technische Universität Dortmund Germany Faculty of Chemical and Food Technology Slovak University of Technology in Bratislava Slovakia

In this paper an approach is studied to guaranteed (set-membership) state estimation for robust output-feedback model predictive control (MPC) with hard input and state constraints. Uncertainties are assumed to arise in a dynamic system from unknown initial conditions of state variables and due to unknown-but-bounded measurement noise. The uncertainty in the state variables is represented as a parallelotopic set. The employed state-estimation algorithm recursively outbounds the feasible set that is given by an intersection of model predictions with obtained measurement information. Along with the well-known minimum-volume criterion for parallelotopic outbounding, three alternative criteria are proposed and studied. The aim is to identify the best outbounding approach for improving performance of the robust MPC. © 2018

关键词： Feedback Model predictive control Predictive control systems Robust control state estimation Uncertainty analysis estimation algorithm Improving performance Input and state constraints Measurement information Output feedback Robust output feedbacks state estimation algorithms Unknown but bounded

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：