Download or read book Adaptive optimal Neurocontrol Based on Adaptive Critic Designs for Synchronous Generators and Facts Devices in Power Systems Using Artificial Neural Networks written by Jung Wook Park and published by . This book was released on 2003 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Advances in Neural Networks Research written by D.C. Wunsch II and published by Elsevier. This book was released on 2003-08-22 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: IJCNN is the flagship conference of the INNS, as well as the IEEE Neural Networks Society. It has arguably been the preeminent conference in the field, even as neural network conferences have proliferated and specialized. As the number of conferences has grown, its strongest competition has migrated away from an emphasis on neural networks. IJCNN has embraced the proliferation of spin-off and related fields (see the topic list, below), while maintaining a core emphasis befitting its name. It has also succeeded in enforcing an emphasis on quality.
Download or read book Power Plants and Power Systems Control 2003 written by Kwang Y Lee and published by Elsevier. This book was released on 2004-04 with total page 1248 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Adaptive Critic Designs Based Neurocontrollers for Local and Wide Area Control of a Multimachine Power System with a Static Compensator written by Salman Mohagheghi and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern power systems operate much closer to their stability limits than before. With the introduction of highly sensitive industrial and residential loads, the loss of system stability becomes increasingly costly. Reinforcing the power grid by installing additional transmission lines, creating more complicated meshed networks and increasing the voltage level are among the effective, yet expensive solutions. An alternative approach is to improve the performance of the existing power system components by incorporating more intelligent control techniques. This can be achieved in two ways: introducing intelligent local controllers for the existing components in the power network in order to employ their utmost capabilities, and implementing global intelligent schemes for optimizing the performance of multiple local controllers based on an objective function associated with the overall performance of the power system. Both these aspects are investigated in this thesis. In the first section, artificial neural networks are adopted for designing an optimal nonlinear controller for a static compensator (STATCOM) connected to a multimachine power system. The neurocontroller implementation is based on the adaptive critic designs (ACD) technique and provides an optimal control policy over the infinite horizon time of the problem. The ACD based neurocontroller outperforms a conventional controller both in terms of improving the power system dynamic stability and reducing the control effort required. The second section investigates the further improvement of the power system behavior by introducing an ACD based neurocontroller for hierarchical control of a multimachine power system. The proposed wide area controller improves the power system dynamic stability by generating optimal control signals as auxiliary reference signals for the synchronous generators2 automatic voltage regulators and the STATCOM line voltage controller. This multilevel hierarchical control scheme forces the different controllers throughout the power system to optimally respond to any fault or disturbance by reducing a predefined cost function associated with the power system performance.
Download or read book American Doctoral Dissertations written by and published by . This book was released on 2002 with total page 776 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Conference Record Industry Applications Society IEEE IAS Annual Meeting 1981 written by IEEE Industry Applications Society and published by . This book was released on 1999 with total page 760 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applications of Neural Adaptive Control Technology written by Jens Kalkkuhl and published by World Scientific. This book was released on 1997 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.
Download or read book Power System Stabilization Using Neural Networks written by Wenxin Liu and published by . This book was released on 2005 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This dissertation includes three papers on power system stabilization using neural network based controllers. Conventional power system stabilizers (CPSSs) are based on linearized models and their parameters are fine tuned to provide good performance around an operating point. At other operating points, the performance of the CPSS degrades. To overcome the drawbacks of CPSS, the first paper presents the design of a continual online trained indirect adaptive neural network (IANN) controller for a single machine infinite bus power system. The second paper presents the design of a nonlinear optimal neurocontroller using adaptive critic designs, combining the concepts of approximate dynamic programming and reinforcement learning, for power system stabilization ... The third paper presents the design of a direct NN controller with stability analysis for a single machine power system"--Abstract, leaf iv.
Download or read book Neurocontrol written by Tomas Hrycej and published by Wiley-Interscience. This book was released on 1997-09-08 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to the design and implementation of successful neurocontrol applications Neurocontrol: Towards an Industrial Control Methodology is the first and only volume that presents a unified framework for neural network-based techniques. It demystifies neurocontroller design and promotes the broad application of neurocontrol to nonlinear control problems. Divided into two major parts —the theoretical and the practical —this book links neurocontrol with the concepts of classical control theory, describes the steps necessary to implement a working algorithm, and provides the information necessary to develop competitive applications of industrial size and complexity. Throughout, the focus is on the most important issues faced by control systems engineers working in this area, including Fundamental approaches to neurocontrol viewed as optimization tasks Neural network architectures for neurocontrol Learning algorithms viewed as optimization algorithms Identification of plant models from measured data Training of an optimal neurocontroller Robustness, adaptiveness, stability, and other special topics Implementation of neurocontrol applications Supplemented with case studies of real-world industrial control applications —from car drive train control to wastewater treatment plant control —Neurocontrol is an important professional reference for control engineers in a wide range of industries as well as for automatic control and adaptive control researchers. It is also an excellent text for graduate and senior undergraduate students in neurocontrol and automatic control.
Download or read book Neural Adaptive Control Technology written by Rafa? ?bikowski and published by World Scientific. This book was released on 1996 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an outgrowth of the workshop on Neural Adaptive Control Technology, NACT I, held in 1995 in Glasgow. Selected workshop participants were asked to substantially expand and revise their contributions to make them into full papers.The workshop was organised in connection with a three-year European Union funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland). A major aim of the NACT project is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from Daimler-Benz.In the book emphasis is put on development of sound theory of neural adaptive control for nonlinear control systems, but firmly anchored in the engineering context of industrial practice. Therefore the contributors are both renowned academics and practitioners from major industrial users of neurocontrol.
Download or read book Optimal Control of Impulsive Systems Using Adaptive Critic Based Neural Networks written by Xiaohua Wang and published by . This book was released on 2008 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This dissertation presents systematic computational tools for the optimal control synthesis of fixed-time and variable-time impulsive systems. Necessary conditions for optimality have been derived for a fixed-time and a variable-time impulsive system using the calculus of variations method. Properties of the costates and the states relation are studied and presented in theorems for the optimal control of a linear fixed-time impulsive system. Optimal control of a variable-time impulsive problem is investigated. A single neural network adaptive critic (SNAC) method for an impulsive system is developed. Algorithms are presented for calculating the optimal impulsive solutions in finite and infinite horizon cases. Since the construction of the networks and the synthesis of the controllers are relatively free of problem-specific assumptions, the method presented here is suitable for a wide range of real life nonlinear impulsive systems. Linear and nonlinear examples of impulsive systems with continuous and impulsive dynamics are considered for the proposed method and algorithms. The given examples show that the proposed method provides the optimal solution for finite and infinite horizon cases"--Abstract, leaf iii.
Download or read book Neural Systems for Control written by Omid Omidvar and published by Elsevier. This book was released on 1997-02-24 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. - Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory - Represents the most up-to-date developments in this rapidly growing application area of neural networks - Takes a new and novel approach to system identification and synthesis
Download or read book Neural Network Vector Control Applications in Power System and Machine Drives written by Xingang Fu and published by . This book was released on 2015 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research investigates how to develop novel neural network vector control technology for Electric Power and Energy System Applications including grid-connected converters (GCC) and Electric Machines to overcome the drawback of conventional vector control methods and to improve the efficiency, reliability, stability, and power quality of electromechanical energy systems. The proposed neural network vector control was developed based on adaptive dynamic programming (ADP) principles to implement the optimal control. The new control approach utilizes mathematical optimal control theory and artificial intelligence, which is a new interdisciplinary research field. An examination of optimal control of a grid-connected converter (GCC) based on heuristic dynamic programming (HDP), which is a basic class of adaptive critic designs (ACDs), was conducted in this dissertation. The difficulty of training recurrent neural networks (RNNs) inspired the development of a novel training algorithm, that is, Levenberg-Marquardt ( LM) + Forward Accumulation Through Time (FATT). With the success of the new training algorithm, the difficulty of training a recurrent neural network has been solved to a large extent. The detailed neural network vector control structures were developed for different applications in power systems including three-phase LCL based grid-connected converters, single phase grid-connected converters with different filters, and in machine drive applications such as three phase squirrel-cage induction motors and doubly fed induction generators (DFIGs). Each of theseapplications has its own emphasis and features, e.g. , the resonance phenomenon associated with LCL filter, the rotor position estimation of induction motor and so on. Both simulations and hardware experiments demonstrated that the proposed ADP-based neural network control technologies produce superior performance to conventional vector control technology and approximates optimal control. Among all the advantages, one of most outstanding features of neural network control is that it can tolerate a wide range of system parameter changes, which is strongly needed in real applications. The proposed technologies provide the prospect to overcome the deficiencies of standard vector control technology and offers high performance control solutions for broad application areas in electric power and energy systems.
Download or read book Adaptive Control with Recurrent High order Neural Networks written by George A. Rovithakis and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.
Download or read book Nonlinear Neural Control with Power Systems Applications written by Dingguo Chen and published by . This book was released on 1998 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extensive studies have been undertaken on the transient stability of large interconnected power systems with flexible ac transmission systems (FACTS) devices installed. Varieties of control methodologies have been proposed to stabilize the postfault system which would otherwise eventually lose stability without a proper control. Generally speaking, regular transient stability is well understood, but the mechanism of load-driven voltage instability or voltage collapse has not been well understood. The interaction of generator dynamics and load dynamics makes synthesis of stabilizing controllers even more challenging. There is currently increasing interest in the research of neural networks as identifiers and controllers for dealing with dynamic time-varying nonlinear systems. This study focuses on the development of novel artificial neural network architectures for identification and control with application to dynamic electric power systems so that the stability of the interconnected power systems, following large disturbances, and/or with the inclusion of uncertain loads, can be largely enhanced, and stable operations are guaranteed. The latitudinal neural network architecture is proposed for the purpose of system identification. It may be used for identification of nonlinear static/dynamic loads, which can be further used for static/dynamic voltage stability analysis. The properties associated with this architecture are investigated. A neural network methodology is proposed for dealing with load modeling and voltage stability analysis. Based on the neural network models of loads, voltage stability analysis evolves, and modal analysis is performed. Simulation results are also provided. The transient stability problem is studied with consideration of load effects. The hierarchical neural control scheme is developed. Trajectory-following policy is used so that the hierarchical neural controller performs as almost well for non-nominal cases as they do for the nominal cases. The adaptive hierarchical neural control scheme is also proposed to deal with the time-varying nature of loads. Further, adaptive neural control, which is based on the on-line updating of the weights and biases of the neural networks, is studied. Simulations provided on the faulted power systems with unknown loads suggest that the proposed adaptive hierarchical neural control schemes should be useful for practical power applications.
Download or read book High level Feedback Control With Neural Networks written by Young Ho Kim and published by World Scientific. This book was released on 1998-09-28 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.
Download or read book Neural Network Based Robust Nonlinear Control written by Nishant Unnikrishnan and published by . This book was released on 2006 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Online trained neural networks have become popular in recent years in the design of robust and adaptive controllers for dynamic systems with uncertainties due to their universal function approximation capabilities. This research explores the application of online neural networks for the design of model following controllers and for dynamic reoptimization of a Single Network Adaptive Critic (SNAC) optimal controller. Model following controllers for a general class of nonlinear systems with unknown uncertainties in their modeling equations have been developed in this research. A desirable characteristic of the model following controller scheme elaborated in this work is that it can be used in conjunction with any known control design technique. This research also discusses a technique that dynamically re-optimizes a Single Network Adaptive Critic controller. The SNAC based optimal controller designed for the nominal plant model no more retains optimality in the presence of uncertainties/unmodeled dynamics that may creep up in the system equations during operation. This necessitates the application of online function approximating neural networks that can help in SNAC reoptimization. Neural network weight update rules for continuous and discrete time systems have been derived using Lyapunov theory that guarantees both the stability of error dynamics and boundedness of the neural network weights. Detailed proofs and numerical simulations of the online weight update rules on various engineering problems have been provided in this document"--Abstract, leaf iii.
