Dynamic estimation and control is a fast growing and widely researched field of study that lays the foundation for a new generation of technologies that can dynamically, adaptively and automatically stabilize power systems. This book provides a comprehensive introduction to research techniques for real-time estimation and control of power systems. Dynamic Estimation and Control of Power Systems coherently and concisely explains key concepts in a step by step manner, beginning with the fundamentals and building up to the latest developments of the field. Each chapter features examples to illustrate the main ideas, and effective research tools are presented for signal processing-based estimation of the dynamic states and subsequent control, both centralized and decentralized, as well as linear and nonlinear. Detailed mathematical proofs are included for readers who desire a deeper technical understanding of the methods. This book is an ideal research reference for engineers and researchers working on monitoring and stability of modern grids, as well as postgraduate students studying these topics. It serves to deliver a clear understanding of the tools needed for estimation and control, while also acting as a basis for readers to further develop new and improved approaches in their own research. - Offers the first concise, single resource on dynamic estimation and control of power systems - Provides both an understanding of estimation and control concepts and a comparison of results - Includes detailed case-studies, including MATLAB codes, to explain and demonstrate the concepts presented
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.
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
Offering an up-to-date account of the strategies utilized in state estimation of electric power systems, this text provides a broad overview of power system operation and the role of state estimation in overall energy management. It uses an abundance of examples, models, tables, and guidelines to clearly examine new aspects of state estimation, the testing of network observability, and methods to assure computational efficiency. Includes numerous tutorial examples that fully analyze problems posed by the inclusion of current measurements in existing state estimators and illustrate practical solutions to these challenges. Written by two expert researchers in the field, Power System State Estimation extensively details topics never before covered in depth in any other text, including novel robust state estimation methods, estimation of parameter and topology errors, and the use of ampere measurements for state estimation. It introduces various methods and computational issues involved in the formulation and implementation of the weighted least squares (WLS) approach, presents statistical tests for the detection and identification of bad data in system measurements, and reveals alternative topological and numerical formulations for the network observability problem.
The operation of distribution networks is becoming increasingly volatile, due to fast variations of renewables and, hence, net-loading conditions. To perform a reliable state estimation under these conditions, this paper considers the case where measurements from meters, phasor measurement units, and distributed energy resources are collected and processed in real time to produce estimates of the state at a fast time scale. Streams of measurements collected in real time and at heterogenous rates render the underlying processing asynchronous, and poses severe strains on workhorse state estimation algorithms. In this work, a real-time state estimation algorithm is proposed, where data are processed on the fly. Starting from a regularized least-squares model, and leveraging appropriate linear models, the proposed scheme boils down to a linear dynamical system where the state is updated based on the previous estimate and on the measurement gathered from a few available sensors. The estimation error is shown to be always bounded under mild condition. Numerical simulations are provided to corroborate the analytical findings.
A guide to the role of static state estimation in the mitigation of potential system failures With contributions from a noted panel of experts on the topic, Advances in Electric Power and Energy: Static State Estimation addresses the wide-range of issues concerning static state estimation as a main energy control function and major tool for evaluating prevailing operating conditions in electric power systems worldwide. This book is an essential guide for system operators who must be fully aware of potential threats to the integrity of their own and neighboring systems. The contributors provide an overview of the topic and review common threats such as cascading black-outs to model-based anomaly detection to the operation of micro-grids and much more. The book also includes a discussion of an effective mathematical programming approach to state estimation in power systems. Advances in Electric Power and Energy reviews the most recent developments in the field and: Offers an introduction to the topic to help non-experts (and professionals) get up-to-date on static state estimation Covers the essential information needed to understand power system state estimation written by experts on the subject Discusses a mathematical programming approach Written for electric power system planners, operators, consultants, power system software developers, and academics, Advances in Electric Power and Energy is the authoritative guide to the topic with contributions from experts who review the most recent developments.
This comprehensive book systematically introduces Dynamic Data Driven Simulation (DDDS) as a new simulation paradigm that makes real-time data and simulation model work together to enable simulation-based prediction/analysis.The text is significantly dedicated to introducing data assimilation as an enabling technique for DDDS. While data assimilation has been studied in other science fields (e.g., meteorology, oceanography), it is a new topic for the modeling and simulation community.This unique reference text bridges the two study areas of data assimilation and modelling and simulation, which have been developed largely independently from each other.
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
We articulate the reason why dynamic state estimation is needed to push the boundaries of the modal analysis of electric power grids in real-time operation. Then, we demonstrate how to unravel linear and nonlinear modes by using the extended dynamic mode decomposition along with estimates of the synchronous generators' rotor angles and rotor speed deviations from nominal speed. The estimated modes are associated with electromechanical oscillations that take place continuously in electric power grids because of imbalances between power generation and demand. The numerical simulations are performed on a synthetic, albeit realistic, 2,000-bus network that was designed to resemble the electric power grid of Texas.
This book describes how dynamic state estimation application in wide-area measurement systems (WAMS) are crucial for power system reliability, to acquire precisely power system dynamics. The event trigger DSE techniques described by the authors provide a design balance between the communication rate and estimation performance, by selectively sending the innovational data. The discussion also includes practical problems for smart grid applications, such as the non-Gaussian process/measurement noise, packet dropout, computation burden of accurate DSE, robustness to the system variation, etc. Readers will learn how the event trigger DSE can facilitate the effective reduction of communication rates, with guaranteed accuracy under a variety of practical conditions in smart grid applications.
