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Power system optimization and coordination of damping controls by series FACTS devices

Power system optimization and coordination of damping contro...

引用

IEEE Power engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Jung-Wook Park R.G. Harley G.K. Venayagamoorthy LG Electronics Inc. Seoul South Korea School of Electrical and Computer Engineering Georgia Institute of Technology USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA

Controllable series line compensation by series flexible ac transmission system (FACTS) devices can provide significant transient stability improvement and effective power oscillation damping. The work presented in this paper gives an overview of control issues of series FACTS devices contributing to stable power system operation when applying suitable controllers with appropriately selected system measurements. Firstly, this paper describes two internal control schemes, which are based on the automatic power flow control mode and line impedance compensation mode, for a switching converter type based series FACTS device. Secondly, it shows that the external (secondary) control can give a solution for overall system optimization by providing well-established supplementary controls and adaptive reference changes to the internal controller. Case studies are presented with simulation results by applying the proposed controllers to the series FACTS device on a power system

关键词： Power systems Damping Control systems Automatic control Power system stability Power system transients Flexible AC transmission systems Power system simulation Power system measurements Power system control

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An adaptive Mamdani fuzzy logic based controller for a static compensator in a multimachine power system

An adaptive Mamdani fuzzy logic based controller for a stati...

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International Conference on intelligent systems Applications to Power systems (ISAP)

作者： S. Mohagheghi G.K. Venayagamoorthy R.G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA University of KwaZulu-Natal South Africa

An adaptive Mamdani based fuzzy logic controller has been designed for controlling a static compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional PI controller during dynamic and transient disturbances

关键词： Programmable control Adaptive control Fuzzy logic Control systems STATCOM Power systems Automatic voltage control Power system modeling Adaptive systems Error correction

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A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with STATCOM

A comparison of PSO and backpropagation for training RBF neu...

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IEEE Swarm Intelligence Symposium, SIS

作者： S. Mohaghegi Y. del Valle G.K. Venayagamoorthy R.G. Harley School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO USA Professor Emeritus University of KwaZulu-Natal South Africa

Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

关键词： Neural networks Power systems Automatic voltage control Artificial neural networks Backpropagation algorithms Convergence Radial basis function networks Particle swarm optimization STATCOM Robustness

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Monotonic convergent iterative learning controller design with iteration varying model uncertainty

Monotonic convergent iterative learning controller design wi...

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IEEE International Conference on Mechatronics and Automation

作者： Hyo-Sung Ahn K.L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems CSOIS Department of Electrical and Computer Engineering Utah State University Logan UT USA Research and Technology Development Center Applied Physics Laboratory Johns Hopkins University Laurel MD USA Center for Self-Organizing and Intelligent Systems CSOIS Department of Electrical and Computer Engineering Utah State University Laurel UT USA

In iterative learning control, model uncertainty issue has been studied in various ways. But, in existing works, the model uncertainty has been considered as iteration invariant, that is, it does not change on the iteration axis. Furthermore, in these works, monotone convergence problem has not been properly studied. In this paper, new monotonic convergence condition of iteration varying model uncertain iterative learning control system is derived. So, the main goal of this paper is to guarantee the monotone convergence of the iteration varying model uncertainty system until the tracking error is reduced below the previously calculated base-line error boundary. Through the numerical example, the validity of the suggested method is illustrated.

关键词： Uncertainty Convergence intelligent systems Physics computing Error correction Laboratories Control system synthesis History Testing Equations

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Transference of Evolved Unmanned Aerial Vehicle Controllers to a Wheeled Mobile Robot

Transference of Evolved Unmanned Aerial Vehicle Controllers ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： G.J. Barlow L.S. Mattos E. Grant C.K. Oh Center for Robotics and Intelligent Machines Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA U.S. Naval Research Laboratory Washington D.C. USA

Transference of controllers evolved in simulation to real vehicles is an important issue in evolutionary robotics (ER). We have previously evolved autonomous navigation controllers for fixed wing UAV applications using multi-objective genetic programming (GP). Controllers were evolved to locate a radar source, navigate the UAV to the source efficiently using on-board sensor measurements, and circle around the emitter. We successfully tested an evolved UAV controller on a wheeled mobile robot. A passive sonar system on the robot was used in place of the radar sensor, and a speaker emitting a tone was used as the target in place of a radar. Using the evolved navigation controller, the mobile robot moved to the speaker and circled around it. The results from this experiment demonstrate that our evolved controllers are capable of transference to real vehicles. Future research will include testing the best evolved controllers by using them to fly real UAVs.

关键词： Unmanned aerial vehicles Mobile robots Sonar navigation Robot control Robot sensing systems Genetic programming Testing Passive radar Remotely operated vehicles Erbium

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Iterative MOESP Type Algorithm for Discrete Time Variant System Identification

Iterative MOESP Type Algorithm for Discrete Time Variant Sys...

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IEEE International Symposium on intelligent Control (ISIC)

作者： A.D.R. Tamariz C.P. Bottura G. Barreto Nucleus of Research and Development in Computer Science University Candido Mendes Rio de Janeiro Brazil UNICAMP School of Electrical and Computer Engineering Control and Intelligent Systems Laboratory State University of Campinas Sao Paulo Brazil

With the main objective to develop an iterative state-space identification algorithm for linear multivariable discrete time-variant systems, in this study we propose and implement a computational procedure we call MOESP-VAR that is based in subspace methods of the type "multivariable output-error state space" (MOESP). In addition the algorithm is tested with criterion and experimentation that we also propose

关键词： Iterative algorithms System identification Time varying systems Vectors Iterative methods Reactive power Testing State-space methods Control engineering Nonlinear systems

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Learning distributed grasp in presence of redundant agents

Learning distributed grasp in presence of redundant agents

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： A.H. Elahibakhsh M. Nili Ahmadabadi F. Janabi-Sharifi B. Nadjar Araabi AI & Robotics Laboratory Center of Excellence for Control and Intelligent Processing Department of Electrical and Computer Eng. Institute for Studies on Theoretical Physics and Mathematics University of Tehran Tehran Iran Robotics and Manufacturing Automation Lab Department of Mechanical and Industrial Engineering Ryerson University Toronto ONT Canada

Learning distributed object grasp by a group of robots with redundant members is the main focus of this paper. In Elahibakhsh, A. H., et al. (2004), we tackled the problem of learning form closure grasp for planar convex objects by multiple non-communicating robots without any information about the shape of objects. In this paper, the problem in presence of redundant agents is investigated. Agents' states and actions are designed such that the group learns grasping different objects using Q-learning method. As the environment is not intelligent enough to assess each agent's effect on the team performance, a credit assignment algorithm based on knowledge evaluation is designed. The proposed method considers the environment credit for the team, number of redundant agents, and the expertness level of each agent in its credit assignment. Applicability of the designed approach is verified through a set of simulations. It is shown that the team learns grasping different objects. Therefore, it is expected that the proposed method can be extended for distributed grasp of deformable objects

关键词： intelligent robots Learning Robot sensing systems Shape Cognitive robotics robotics and automation intelligent agent Algorithm design and analysis Grasping Redundancy

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Stability analysis of iterative learning control system with interval uncertainty

Stability analysis of iterative learning control system with...

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作者： Ahn, Hyo-Sung Moore, Kevin L. Chen, Yang Quan Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160 United States Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099 United States

ISBN: (纸本)008045108X

This paper presents a stability analysis of the iterative learning control (ILC) problem when the plant Markov parameters are subject to interval uncertainty. Using the super-vector approach to ILC, vertex Markov matrices are employed to develop sufficient conditions for both asymptotic stability and monotonic convergence of the ILC process. It is shown that Kharitonov segments between vertex matrices are not required for checking the stability of interval super-vector ILC systems, but instead checking just the vertex Markov matrices is sufficient. Copyright © 2005 IFAC.

关键词： Asymptotic stability

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Range identification for perspective dynamic systems with 3D imaging surfaces

Range identification for perspective dynamic systems with 3D...

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2005 American Control Conference, ACC

作者： Ma, Lili Chen, YangQuan Moore, Kevin L. 4160 Old Main Hill Logan UT 84322-4160 United States Center for Self-Organizing and Intelligent Systems Dept. of Electrical and Computer Engineering Utah State University 4160 Old Main Hill Logan UT 84322-4160 Research and Technology Development Center Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Road Laurel MD 20723-6099

Range identification using image sequence via observations from a traditional camera-type vision system has been discussed in the literature. In this paper, the camera-type planar imaging surface is extended to several well-known 3D surfaces, such as an arbitrary plane, a sphere, an ellipsoid, and a paraboloid. For a general imaging surface, the resulting perspective dynamic system is not guaranteed to preserve an affine form. In this case, most existing nonlinear observers that are applicable to the perspective dynamic system observed via a camera can not be applied for the range identification problem directly. We show via simulations that our recently proposed linear approximation observer can perform the state estimation. © 2005 AACC.

关键词： Image segmentation

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Monotonic Convergent Iterative Learning Controller Design Based on Interval Model Conversion

Monotonic Convergent Iterative Learning Controller Design Ba...

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IEEE International Symposium on intelligent Control (ISIC)

作者： Hyo-Sung Ahn K.L. Moore YangQuan Chen Center for Self-Organizing and Intelligent Systems (CSOIS) Department of Electrical and Computer Engineering Utah State University Logan UT USA Research and Technology Development Center Applied Physics Laboratory Johns Hopkins University-Applied Physics Laboratory Laurel MD USA

This paper presents the design of a robust iterative learning controller for the case of a plant with interval model uncertainty in the "A-matrix" of its state space description. First order perturbation theory is utilized to find bounds on the eigenvalues and eigenvectors of the powers of A when A is an interval matrix. These bounds are then used for calculation of the interval uncertainty of the Markov matrix, which can then be used to design an iterative learning controller that ensures monotonic convergence for all systems in the interval plant

关键词： Uncertainty State-space methods Matrix converters Convergence Control systems Motion control intelligent systems Physics computing Design engineering USA Councils

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