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Author: N. Priyadharshini Publisher: Alibaba ISBN: 9781805290186 Category : Technology & Engineering Languages : en Pages : 0
Book Description
The Hybrid Kalman Filter for Grid State Estimation is a powerful tool used to monitor, control and stabilize power systems in real-time. In this study, N. Priyadharshini investigates the use of this state-of-the-art technique in the context of power systems, along with optimal placement of phasor measurement units (PMUs) for accurate monitoring. The author emphasizes the importance of accurate state estimation in ensuring grid reliability and stability, especially in the presence of increasing levels of renewable energy integration and distributed energy resources. The hybrid Kalman filter, which combines the advantages of both dynamic and static state estimation, is shown to be an effective algorithm for accurately estimating the state of power systems using synchronized measurements from PMUs. The study also addresses the optimal placement of PMUs for improving system observability and detecting and diagnosing faults in the grid. The placement of PMUs is optimized using observability analysis techniques, and the proposed algorithm is validated through numerical simulations. The study covers various aspects of power system modeling, control, and stability, such as power flow analysis, observability analysis, system dynamics, and fault detection and diagnosis. The application of the hybrid Kalman filter for dynamic state estimation and measurement error correction is thoroughly discussed. Moreover, the study explores the integration of renewable energy sources and microgrids into the power system, and the use of smart grid technologies for enhancing energy efficiency and power quality. Overall, the study provides valuable insights into the use of hybrid Kalman filters for accurate grid state estimation, optimal placement of PMUs, and advanced power system monitoring and control. It is a useful reference for researchers and engineers working in the field of power systems and smart grid technologies.
Author: N. Priyadharshini Publisher: Alibaba ISBN: 9781805290186 Category : Technology & Engineering Languages : en Pages : 0
Book Description
The Hybrid Kalman Filter for Grid State Estimation is a powerful tool used to monitor, control and stabilize power systems in real-time. In this study, N. Priyadharshini investigates the use of this state-of-the-art technique in the context of power systems, along with optimal placement of phasor measurement units (PMUs) for accurate monitoring. The author emphasizes the importance of accurate state estimation in ensuring grid reliability and stability, especially in the presence of increasing levels of renewable energy integration and distributed energy resources. The hybrid Kalman filter, which combines the advantages of both dynamic and static state estimation, is shown to be an effective algorithm for accurately estimating the state of power systems using synchronized measurements from PMUs. The study also addresses the optimal placement of PMUs for improving system observability and detecting and diagnosing faults in the grid. The placement of PMUs is optimized using observability analysis techniques, and the proposed algorithm is validated through numerical simulations. The study covers various aspects of power system modeling, control, and stability, such as power flow analysis, observability analysis, system dynamics, and fault detection and diagnosis. The application of the hybrid Kalman filter for dynamic state estimation and measurement error correction is thoroughly discussed. Moreover, the study explores the integration of renewable energy sources and microgrids into the power system, and the use of smart grid technologies for enhancing energy efficiency and power quality. Overall, the study provides valuable insights into the use of hybrid Kalman filters for accurate grid state estimation, optimal placement of PMUs, and advanced power system monitoring and control. It is a useful reference for researchers and engineers working in the field of power systems and smart grid technologies.
Author: Simon Haykin Publisher: Wiley-Interscience ISBN: 047146421X Category : Technology & Engineering Languages : en Pages : 304
Book Description
State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is almost always linear, this book recognizes and deals with the fact that real problems are most often nonlinear. The first chapter offers an introductory treatment of Kalman filters with an emphasis on basic Kalman filter theory, Rauch-Tung-Striebel smoother, and the extended Kalman filter. Other chapters cover: An algorithm for the training of feedforward and recurrent multilayered perceptrons, based on the decoupled extended Kalman filter (DEKF) Applications of the DEKF learning algorithm to the study of image sequences and the dynamic reconstruction of chaotic processes The dual estimation problem Stochastic nonlinear dynamics: the expectation-maximization (EM) algorithm and the extended Kalman smoothing (EKS) algorithm The unscented Kalman filter Each chapter, with the exception of the introduction, includes illustrative applications of the learning algorithms described here, some of which involve the use of simulated and real-life data. Kalman Filtering and Neural Networks serves as an expert resource for researchers in neural networks and nonlinear dynamical systems. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley Makerting Department.
Author: Geir Evensen Publisher: Springer Science & Business Media ISBN: 3540383018 Category : Science Languages : en Pages : 285
Book Description
This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
Author: Charles K. Chui Publisher: Springer Science & Business Media ISBN: 3540878483 Category : Business & Economics Languages : en Pages : 241
Book Description
This book presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method and an indirect method.
Author: Shingo Watanabe Publisher: ISBN: Category : Languages : en Pages :
Book Description
In this thesis, we propose two novel approaches for hybrid Ensemble Kalman Filter (EnKF) to overcome limitations of the traditional EnKF. The first approach is to swap the ensemble mean for the ensemble mode estimation to improve the covariance calculation in EnKF. The second approach is a coarse scale permeability constraint while updating in EnKF. Both hybrid EnKF approaches are coupled with the streamline based Generalized Travel Time Inversion (GTTI) algorithm for periodic updating of the mean of the ensemble and to sequentially update the ensemble in a hybrid fashion. Through the development of the hybrid EnKF algorithm, the characteristics of the EnKF are also investigated. We found that the limits of the updated values constrain the assimilation results significantly and it is important to assess the measurement error variance to have a proper balance between preserving the prior information and the observation data misfit. Overshooting problems can be mitigated with the streamline based covariance localizations and normal score transformation of the parameters to support the Gaussian error statistics. The swapping mean and mode estimation approach can give us a better matching of the data as long as the mode solution of the inversion process is satisfactory in terms of matching the observation trajectory. The coarse scale permeability constrained hybrid approach gives us better parameter estimation in terms of capturing the main trend of the permeability field and each ensemble member is driven to the posterior mode solution from the inversion process. However the WWCT responses and pressure responses need to be captured through the inversion process to generate physically plausible coarse scale permeability data to constrain hybrid EnKF updating. Uncertainty quantification methods for EnKF were developed to verify the performance of the proposed hybrid EnKF compared to the traditional EnKF. The results show better assimilation quality through a sequence of updating and a stable solution is demonstrated. The potential of the proposed hybrid approaches are promising through the synthetic examples and a field scale application.
Author: N. Priyadharshini
Author: N. Priyadharshini
Author: Sujit Kumar Roy
Author: Simon Haykin
Author: Geir Evensen
Author: Mohinder S. Grewal
Author: Charles K. Chui
Author: Wisdom Mawuli Hokey
Author: Harold Wayne Sorenson
Author: A. M. Smit
Author: Shingo Watanabe