Author: Jian Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
After the deregulation of the power industry, power systems are required to be operated efficiently and economically in today's strongly competitive environment. In order to achieve these objectives, it is crucial for power system control centers to accurately monitor the system operating state. State estimation is an essential tool in an energy management system (EMS). It is responsible for providing an accurate and correct estimate for the system operating state based on the available measurements in the power system. A robust state estimation should have the capability of keeping the system observable during different contingencies, as well as detecting and identifying the gross errors in measurement set and network topology. However, this capability relies directly on the system network configuration and measurement locations. In other words, a reliable and redundant measurement system is the primary condition for a robust state estimation. This dissertation is focused on the possible benefits to state estimation of using synchronized phasor measurements to improve the measurement system. The benefits are investigated with respect to the measurement redundancy, bad data and topology error processing functions in state estimation. This dissertation studies how to utilize the phasor measurements in the traditional state estimation. The optimal placement of measurement to realize the maximum benefit is also considered and practical algorithms are designed. It is shown that strategic placement of a few phasor measurement units (PMU) in the system can significantly increase measurement redundancy, which in turn can improve the capability of state estimation to detect and identify bad data, even during loss of measurements. Meanwhile, strategic placement of traditional and phasor measurements can also improve the state estimation's topology error detection and identification capability, as well as its robustness against branch outages. The proposed procedures and algorithms are illustrated and demonstrated with different sizes of test systems. And numerical simulations verify the gained benefits of state estimation in bad data processing and topology error processing.
Author: Arturo Bretas
Publisher: Elsevier
ISBN: 0323903223
Category : Technology & Engineering
Languages : en
Pages : 294
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Book Description
Cyber-Physical Power System State Estimation updates classic state estimation tools to enable real-time operations and optimize reliability in modern electric power systems. The work introduces and contextualizes the core concepts and classic approaches to state estimation modeling. It builds on these classic approaches with a suite of data-driven models and non-synchronized measurement tools to reflect current measurement trends required by increasingly more sophisticated grids. Chapters outline core definitions, concepts and the network analysis procedures involved in the real-time operation of EPS. Specific sections introduce power flow problem in EPS, highlighting network component modeling and power flow equations for state estimation before addressing quasi static state estimation in electrical power systems using Weighted Least Squares (WLS) classical and alternatives formulations. Particularities of the state estimation process in distribution systems are also considered. Finally, the work goes on to address observability analysis, measurement redundancy and the processing of gross errors through the analysis of WLS static state estimator residuals. Develops advanced approaches to smart grid real-time monitoring through quasi-static model state estimation and non-synchronized measurements system models Presents a novel, extended optimization, physics-based model which identifies and corrects for measurement error presently egregiously discounted in classic models Demonstrates how to embed cyber-physical security into smart grids for real-time monitoring Introduces new approaches to calculate power flow in distribution systems and for estimating distribution system states Incorporates machine-learning based approaches to complement the state estimation process, including pattern recognition-based solutions, principal component analysis and support vector machines
Author: Timothy D. Barfoot
Publisher: Cambridge University Press
ISBN: 1107159393
Category : Computers
Languages : en
Pages : 381
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Book Description
A modern look at state estimation, targeted at students and practitioners of robotics, with emphasis on three-dimensional applications.
Author: Liuxi Zhang
Publisher:
ISBN:
Category : Electric power systems
Languages : en
Pages : 108
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Book Description
State Estimation plays a significant role in power systems secure operation. It performs real time monitoring of the entire system and provides the system states to other security applications. Recently, the Phasor Measurement Units (PMUs) have been invented and deployed to power systems to provide both phasor magnitudes and phase angles. This research focuses on enhancing power system state estimation and parameter error identification through innovative use of phasor measurements. The first part of the dissertation focuses on improving network parameter error identification through innovative use of phasor measurements. Previous work has shown that the parameter errors in certain topologies could not be detected or identified without incorporating phasor measurements. This dissertation firstly investigates a computationally efficient algorithm to identify all such topologies for a given system. Then a strategic phasor measurement placement is proposed to ensure detectability and identifiability of certain network parameter errors. In addition, this method is reformulated and extended to detect and identify isolated power islands after disturbances. Another way to improve parameter error identification is to use multiple measurement scans instead of the normal single measurement scan. This dissertation investigates an alternative approach using multiple measurement scans. It addresses limitations for parameter error in certain topologies without investing new measurements. The second part of the dissertation concentrates on interoperability of PMUs in state estimation. Incorporating phasor measurements into existing Weighted Least Squares (WLS) state estimation brings up the interoperability issue about how to choose the right measurement weights for different types of PMUs. This part develops an auto tuning algorithm which requires no initial information about the phasor measurement accuracies. This algorithm is applied to tune the state estimator to update the weights of different types of PMUs in order to have a consistent numerically stable estimation solution. Furthermore, the impact of this tuning method on bad measurement detection is investigated. All these methods have been tested in IEEE standard systems to show their performance.
Author: Mohamad Hasan Modir-Shanechi
Publisher:
ISBN:
Category : Electric generators
Languages : en
Pages : 280
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Book Description
Author: Ferdinand van der Heijden
Publisher: John Wiley & Sons
ISBN: 0470090146
Category : Science
Languages : en
Pages : 440
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Book Description
Classification, Parameter Estimation and State Estimation is a practical guide for data analysts and designers of measurement systems and postgraduates students that are interested in advanced measurement systems using MATLAB. 'Prtools' is a powerful MATLAB toolbox for pattern recognition and is written and owned by one of the co-authors, B. Duin of the Delft University of Technology. After an introductory chapter, the book provides the theoretical construction for classification, estimation and state estimation. The book also deals with the skills required to bring the theoretical concepts to practical systems, and how to evaluate these systems. Together with the many examples in the chapters, the book is accompanied by a MATLAB toolbox for pattern recognition and classification. The appendix provides the necessary documentation for this toolbox as well as an overview of the most useful functions from these toolboxes. With its integrated and unified approach to classification, parameter estimation and state estimation, this book is a suitable practical supplement in existing university courses in pattern classification, optimal estimation and data analysis. Covers all contemporary main methods for classification and estimation. Integrated approach to classification, parameter estimation and state estimation Highlights the practical deployment of theoretical issues. Provides a concise and practical approach supported by MATLAB toolbox. Offers exercises at the end of each chapter and numerous worked out examples. PRtools toolbox (MATLAB) and code of worked out examples available from the internet Many examples showing implementations in MATLAB Enables students to practice their skills using a MATLAB environment
Author: Chaw-Bing Chang
Publisher: MIT Press
ISBN: 0262548917
Category : Technology & Engineering
Languages : en
Pages : 473
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Book Description
A rigorous introduction to the theory and applications of state estimation and association, an important area in aerospace, electronics, and defense industries. Applied state estimation and association is an important area for practicing engineers in aerospace, electronics, and defense industries, used in such tasks as signal processing, tracking, and navigation. This book offers a rigorous introduction to both theory and application of state estimation and association. It takes a unified approach to problem formulation and solution development that helps students and junior engineers build a sound theoretical foundation for their work and develop skills and tools for practical applications. Chapters 1 through 6 focus on solving the problem of estimation with a single sensor observing a single object, and cover such topics as parameter estimation, state estimation for linear and nonlinear systems, and multiple model estimation algorithms. Chapters 7 through 10 expand the discussion to consider multiple sensors and multiple objects. The book can be used in a first-year graduate course in control or system engineering or as a reference for professionals. Each chapter ends with problems that will help readers to develop derivation skills that can be applied to new problems and to build computer models that offer a useful set of tools for problem solving. Readers must be familiar with state-variable representation of systems and basic probability theory including random and stochastic processes.
Author: Matteo Paris
Publisher: Springer Science & Business Media
ISBN: 9783540223290
Category : Science
Languages : en
Pages : 548
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Book Description
This book is a comprehensive survey of most of the theoretical and experimental achievements in the field of quantum estimation of states and operations. Albeit still quite young, this field has already been recognized as a necessary tool for research in quantum optics and quantum information, beyond being a fascinating subject in its own right since it touches upon the conceptual foundations of quantum mechanics. The book consists of twelve extensive lectures that are essentially self-contained and modular, allowing combination of various chapters as a basis for advanced courses and seminars on theoretical or experimental aspects. The last two chapters, for instance, form a self-contained exposition on quantum discrimination problems. The book will benefit graduate students and newcomers to the field as a high-level but accessible textbook, lecturers in search for advanced course material and researchers wishing to consult a modern and authoritative source of reference.
Author: Dan Simon
Publisher: John Wiley & Sons
ISBN: 0470045337
Category : Technology & Engineering
Languages : en
Pages : 554
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Book Description
A bottom-up approach that enables readers to master and apply the latest techniques in state estimation This book offers the best mathematical approaches to estimating the state of a general system. The author presents state estimation theory clearly and rigorously, providing the right amount of advanced material, recent research results, and references to enable the reader to apply state estimation techniques confidently across a variety of fields in science and engineering. While there are other textbooks that treat state estimation, this one offers special features and a unique perspective and pedagogical approach that speed learning: * Straightforward, bottom-up approach begins with basic concepts and then builds step by step to more advanced topics for a clear understanding of state estimation * Simple examples and problems that require only paper and pen to solve lead to an intuitive understanding of how theory works in practice * MATLAB(r)-based source code that corresponds to examples in the book, available on the author's Web site, enables readers to recreate results and experiment with other simulation setups and parameters Armed with a solid foundation in the basics, readers are presented with a careful treatment of advanced topics, including unscented filtering, high order nonlinear filtering, particle filtering, constrained state estimation, reduced order filtering, robust Kalman filtering, and mixed Kalman/H? filtering. Problems at the end of each chapter include both written exercises and computer exercises. Written exercises focus on improving the reader's understanding of theory and key concepts, whereas computer exercises help readers apply theory to problems similar to ones they are likely to encounter in industry. With its expert blend of theory and practice, coupled with its presentation of recent research results, Optimal State Estimation is strongly recommended for undergraduate and graduate-level courses in optimal control and state estimation theory. It also serves as a reference for engineers and science professionals across a wide array of industries.
Author: Saurabh Sahasrabuddhe
Publisher:
ISBN:
Category :
Languages : en
Pages : 109
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Book Description
Although the state estimator has been a regular application running in many utility control centers for over four decades, detection and identification of bad data (outliers) among the input measurements continues to be a difficult task. The widely adopted Weighted Least Square formulation of the power system static state estimation is known to be vulnerable to presence of bad data and even a single bad data can significantly impact the solution quality. Since the estimate of system state obtained from state estimation serves as starting point for many security and market related downstream applications that run within a control center, the problem of detection and identification of bad data is important. It has been shown that the traditionally used methods of detection and/or identification of bad data in power system static state estimation suffer from drawbacks like failing to detect mild to medium bad data in leverage measurements and excessive false detection rates. The traditional approach to process multiple bad data has been successive elimination (of single bad data) and re-estimation. This approach is highly computationally intensive and time consuming. The problem of computationally intensive multiple bad data processing has an even greater bearing on the Linear State Estimator, which is expected to run every second or potentially at sub-second intervals in the control centers.The work presented here focuses on the problem of multiple bad data processing in power system static state estimation in two ways. Firstly, use of Custom Thresholds is proposed for detection of bad data. The Custom Thresholds are shown to exhibit better false detection performance while being sensitive to mild bad data, even in leverage measurements. Secondly, a new algorithm is proposed to identify the culprit bad measurements. The proposed algorithm utilizes the very nature of bad data in different types of measurements, to accomplish the processing within few cycles of successive elimination and re-estimation. The efficiency and accuracy of the proposed algorithm is validated through thousands of simulations on various standard test systems. The proposed algorithm can be easily integrated in any commercial Weighted Least Square power system static state estimation - linear or iterative.
