Download or read book Online Neuro adaptive Learning for Power System Dynamic State Estimation written by Rahul Birari and published by . This book was released on 2017 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increased penetration of Distributed Generators (DGs) in the contemporary Power System, having knowledge of rapid Real-Time electro-mechanical Dynamic States has become crucial to ensure the safety and reliability of the grid. In the conventional SCADA based Dynamic State Estimation (DSE) speed was limited by the slow sampling rates (2-4 Hz) so State Estimation was limited to static states such as Voltage and Angle at the buses.
Download or read book Self Learning Optimal Control of Nonlinear Systems written by Qinglai Wei and published by Springer. This book was released on 2017-06-13 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.
Download or read book Modeling Dynamics and Control of Electrified Vehicles written by Haiping Du and published by Woodhead Publishing. This book was released on 2017-10-19 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modelling, Dynamics and Control of Electrified Vehicles provides a systematic overview of EV-related key components, including batteries, electric motors, ultracapacitors and system-level approaches, such as energy management systems, multi-source energy optimization, transmission design and control, braking system control and vehicle dynamics control. In addition, the book covers selected advanced topics, including Smart Grid and connected vehicles. This book shows how EV work, how to design them, how to save energy with them, and how to maintain their safety. The book aims to be an all-in-one reference for readers who are interested in EVs, or those trying to understand its state-of-the-art technologies and future trends. - Offers a comprehensive knowledge of the multidisciplinary research related to EVs and a system-level understanding of technologies - Provides the state-of-the-art technologies and future trends - Covers the fundamentals of EVs and their methodologies - Written by successful researchers that show the deep understanding of EVs
Download or read book Robust Dynamic State Estimation of Power Systems written by Junbo Zhao and published by Elsevier. This book was released on 2023-06-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust Dynamic State Estimation of Power Systems demonstrates how to implement and apply robust dynamic state estimators to problems in modern power systems, thereby bridging the literatures of dynamic state estimation and robust estimation theory. The book presents Kalman filter algorithms, demonstrating how to build powerful, robust counterparts. Following sections build out case study-based implementations of robust Kalman filters to decontextualized applications across dynamic state estimation in power systems. Coverage encompasses theoretical backgrounds, motivations, problem formulation, implementations, uncertainties, anomalies and practical applications, such as generator parameter calibration, unknown inputs estimation, control failure detection, protection, and cyberattack detection. Future research topics are identified and discussed, including open research questions. The book will serve as a key reference for power system real-time monitoring, control center engineers, and graduate students for learning (course related work) and research. Elucidates theoretical motivations, definitions, formulations, and robustness enhancement Engages with emerging practical problems in the application of dynamic state estimation through case studies Provides a roadmap for the transition of DSE concepts to practical implementations and applications Develops advanced robust statistics theory and uncertainty management methods
Download or read book Power System Dynamic State Estimation Practical Analysis written by G. Lozza and published by . This book was released on 1977 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modularized Global Dynamic State Estimation for Power Systems written by Mohamad Hasan Modir-Shanechi and published by . This book was released on 1979 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic State Estimation in Nonlinear Multi machine Power Systems written by William Langhorne Miller and published by . This book was released on with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book New Trends in Observer based Control written by Olfa Boubaker and published by Academic Press. This book was released on 2019-08-23 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: New Trends in Observer-Based Control: A Practical Guide to Process and Engineering Applications presents a concise introduction to the latest advances in observer-based control design. The book gives a comprehensive tutorial on new trends in the design of observer-based controllers for which the separation principle is well established. It covers a wide range of applications, also including worked examples that make it ideal for both advanced courses and researchers starting work in the field. This book is also particularly suitable for engineers who want to quickly and efficiently enter the field. - Presents a clear-and-concise introduction to the latest advances in observer-based control design - Offers content on many facets of observer-based control design - Discusses key applications in the fields of power systems, robotics and mechatronics, flight and automotive systems
Download or read book Control and Nonlinear Dynamics on Energy Conversion Systems written by Herbert Ho-Ching Iu and published by MDPI. This book was released on 2019-07-01 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ever-increasing need for higher efficiency, smaller size, and lower cost make the analysis, understanding, and design of energy conversion systems extremely important, interesting, and even imperative. One of the most neglected features in the study of such systems is the effect of the inherent nonlinearities on the stability of the system. Due to these nonlinearities, these devices may exhibit undesirable and complex dynamics, which are the focus of many researchers. Even though a lot of research has taken place in this area during the last 20 years, it is still an active research topic for mainstream power engineers. This research has demonstrated that these systems can become unstable with a direct result in increased losses, extra subharmonics, and even uncontrollability/unobservability. The detailed study of these systems can help in the design of smaller, lighter, and less expensive converters that are particularly important in emerging areas of research like electric vehicles, smart grids, renewable energy sources, and others. The aim of this Special Issue is to cover control and nonlinear aspects of instabilities in different energy conversion systems: theoretical, analysis modelling, and practical solutions for such emerging applications. In this Special Issue, we present novel research works in different areas of the control and nonlinear dynamics of energy conversion systems.
Download or read book Static and Dynamic State Estimation of Power Systems written by Zhaoyang Jin and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Physics guided Deep Learning for Power System Sate Estimation written by Lei Wang and published by . This book was released on 2019 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conventionally, physics-based models are used for power system state estimation, including Weighted Least Square (WLS) or Weighted Absolute Value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this thesis, a Physics-Guided Deep Learning (PGDL) method incorporating the physical power system model with the deep learning is proposed to improve the performance of power system state estimation. Specifically, inspired by Autoencoders, deep neural networks (DNNs) are utilized to learn the temporal correlations of power system states. The estimated system states are checked against the physics law by a set of power flow equations. Hence, the proposed PGDL approach is both data-driven and physics-based. The proposed method is compared with the traditional methods on the basis of accuracy and robustness in IEEE standard cases. The results indicate that PGDL framework provides more accurate and robust estimation for power system state estimation.
Download or read book Adaptive Dynamic Programming with Applications in Optimal Control written by Derong Liu and published by Springer. This book was released on 2017-01-04 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP approach which is then extended to other branches of control theory including decentralized control, robust and guaranteed cost control, and game theory. In the last part of the book the real-world significance of ADP theory is presented, focusing on three application examples developed from the authors’ work: • renewable energy scheduling for smart power grids;• coal gasification processes; and• water–gas shift reactions. Researchers studying intelligent control methods and practitioners looking to apply them in the chemical-process and power-supply industries will find much to interest them in this thorough treatment of an advanced approach to control.
Download or read book Real Time Dynamic State Estimation written by Safoan Al-halali and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the state estimation algorithm has been firstly proposed, considerable research interest has been shown in adapting and applying the different versions of this algorithm to the power transmission systems. Those applications include power system state estimation (PSSE) and short-term operational planning. In the transmission level, state estimation offers various applications including, process monitoring and security monitoring. Recently, distribution systems experience a much higher level of variability and complexity due to the large increase in the penetration level of distributed energy resources (DER), such as distributed generation (DG), demand-responsive loads, and storage devices. The first step, for better situational awareness at the distribution level, is to adapt the most developed real time state estimation algorithm to distribution systems, including distribution system state estimation (DSSE). DSSE has an important role in the operation of the distribution systems. Motivated by the increasing need for robust and accurate real time state estimators, capable of capturing the dynamics of system states and suitable for large-scale distribution networks with a lack of sensors, this thesis introduces a three state estimators based on a distributed approach. The first proposed estimator technique is the square root cubature Kalman filter (SCKF), which is the improved version of cubature Kalman filter (CKF). The second one is based on a combination of the particle filter (PF) and the SCKF, which yields a square root cubature particle filter (SCPF). This technique employs a PF with the proposal distribution provided by the SCKF. Additionally, a combination of PF and CKF, which yields a cubature particle filter (CPF) is proposed. Unlike the other types of filters, the PF is a non-Gaussian algorithm from which a true posterior distribution of the estimated states can be obtained. This permits the replacement of real measurements with pseudo-measurements and allows the calculation to be applied to large-scale networks with a high degree of nonlinearity. This research also provides a comparison study between the above mentioned algorithms and the latest algorithms available in the literature. To validate their robustness and accuracy, the proposed methods were tested and verified using a large range of customer loads with 50 % uncertainty on a connected IEEE 123-bus system. Next, a developed foretasted aided state estimator is proposed. The foretasted aided state estimator is needed to increase the immunization of the state estimator against the delay and loss of the real measurements, due to the sensors malfunction or communication failure. Moreover, due to the lack of measurements in the electrical distribution system, the pseudo-measurements are needed to insure the observability of the state estimator. Therefore, the very short term load forecasting algorithm that insures the observability and provides reliable backup data in case of sensor malfunction or communication failure is proposed. The proposed very short term load forecasting is based on the wavelet recurrent neural network (WRNN). The historical data used to train the RNN are decomposed into low-frequency, low-high frequency and high frequency components. The neural networks are trained using an extended Kalman filter (EKF) for the low frequency component and using a square root cubature Kalman filter (SCKF) for both low-high frequency and high frequency components. To estimate the system states, state estimation algorithm based SCKF is used. The results demonstrate the theoretical and practical advantages of the proposed methodology. Finally, in recent years several cyber-attacks have been recorded against sensitive monitoring systems. Among them is the automatic generation control (AGC) system, a fundamental control system used in all power networks to keep the network frequency at its desired value and for maintaining tie line power exchanges at their scheduled values. Motivated by the increasing need for robust and safe operation of AGCs, this thesis introduces an attack resilient control scheme for the AGC system based on attack detection using real time state estimation. The proposed approach requires redundancy of sensors available at the transmission level in the power network and leverages recent results on attack detection using mixed integer linear programming (MILP). The proposed algorithm detects and identifies the sensors under attack in the presence of noise. The non-attacked sensors are then averaged and made available to the feedback controller. No assumptions about the nature of the attack signal are made. The proposed method is simulated using a large range of attack signals and uncertain sensors measurements. All the proposed algorithms were implemented in MATLAB to verify their theoretical expectations.
Download or read book Reinforcement Learning and Approximate Dynamic Programming for Feedback Control written by Frank L. Lewis and published by John Wiley & Sons. This book was released on 2013-01-28 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
Download or read book Advanced Planning Control and Signal Processing Methods and Applications in Robotic Systems written by Zhan Li and published by Frontiers Media SA. This book was released on 2022-02-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Index to IEEE Publications written by Institute of Electrical and Electronics Engineers and published by . This book was released on 1998 with total page 1270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues for 1973- cover the entire IEEE technical literature.
Download or read book Advances in Applied Nonlinear Optimal Control written by Gerasimos Rigatos and published by Cambridge Scholars Publishing. This book was released on 2020-11-19 with total page 741 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume discusses advances in applied nonlinear optimal control, comprising both theoretical analysis of the developed control methods and case studies about their use in robotics, mechatronics, electric power generation, power electronics, micro-electronics, biological systems, biomedical systems, financial systems and industrial production processes. The advantages of the nonlinear optimal control approaches which are developed here are that, by applying approximate linearization of the controlled systems’ state-space description, one can avoid the elaborated state variables transformations (diffeomorphisms) which are required by global linearization-based control methods. The book also applies the control input directly to the power unit of the controlled systems and not on an equivalent linearized description, thus avoiding the inverse transformations met in global linearization-based control methods and the potential appearance of singularity problems. The method adopted here also retains the known advantages of optimal control, that is, the best trade-off between accurate tracking of reference setpoints and moderate variations of the control inputs. The book’s findings on nonlinear optimal control are a substantial contribution to the areas of nonlinear control and complex dynamical systems, and will find use in several research and engineering disciplines and in practical applications.
