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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
This book analyzes the secure problems of cyber-physical systems from both the adversary and defender sides. Targeting the challenging security problems of cyber-physical systems under malicious attacks, this book presents some recent novel secure state estimation and control algorithms, in which moving target defense scheme, zero-sum game-theoretical approach, reinforcement learning, neural networks, and intelligent control are adopted. Readers will find not only the valuable secure state estimation and control schemes combined with the approaches aforementioned, but also some vital conclusions for securing cyber-physical systems, for example, the critical value of allowed attack probability, the maximum number of sensors to be attacked, etc. The book also provides practical applications, example of which are unmanned aerial vehicles, interruptible power system, and robot arm to validate the proposed secure algorithms. Given its scope, it offers a valuable resource for undergraduate and graduate students, academics, scientists, and engineers who are working in this field.
Growing system size and complexity along with the large amount of data provided by phasor measurement units (PMUs) are the drivers for accurate state estimation algorithms for online monitoring and operation of power grids. State estimation is the process of estimating unknown state variables in a power grid, which are then used for energy management system (EMS) functions in the control centre. Even with modern computing power, the large volume of computation in the state estimation process is highly time and memory intensive, and a serious bottleneck for online operation and control of the grid. Furthermore, the deployment of new smart grid technologies in communication and smart metering technologies brings new challenges to the state estimation problem. In addition to failure of physical infrastructure, the smart grid is also sensitive to cyber-attacks on its communication layer. Intelligent cyber-attacks can be designed to be unobservable by the traditional bad data detector. Such attacks can significantly impact the decision making process in control centres and can result in potentially catastrophic outcomes. The focus of this research is to design parallel computational algorithms to accelerate dynamic state estimation for both static and dynamic states in large-scale power networks. Moreover, a stochastic model is proposed to guard the system against intelligent cyberattacks. Utilizing the massively parallel architecture of graphic processors and using detailed models of power system components, the proposed research achieved the required accuracy as well as the computational speed-up in the results.
This book documents recent advances in the field of modeling, simulation, control, security and reliability of Cyber- Physical Systems (CPS) in power grids. The aim of this book is to help the reader gain insights into working of CPSs and understand their potential in transforming the power grids of tomorrow. This book will be useful for all those who are interested in design of cyber-physical systems, be they students or researchers in power systems, CPS modeling software developers, technical marketing professionals and business policy-makers.
This book discusses recent advances in cyber-physical power systems (CPPS) in the modeling, analysis and applications of smart grid. It introduces a series of models, such as an analysis of interaction between the power grid and the communication network, differential protection in smart distribution systems, data flow for VLAN-based communication in substations, a co-simulation model for investigating the impacts of cyber-contingency and distributed control systems as well as the analytical techniques used in different parts of cyber physical energy systems. It also discusses methods of cyber-attack on power systems, particularly false data injection. The results presented are a comprehensive summary of the authors’ original research conducted over a period of 5 years. The book is of interest to university researchers, R&D engineers and graduate students in power and energy systems.
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.
Many critical infrastructures, such as transportation and electric energy networks, and health care, are now becoming highly integrated with information and communication technology, in order to be more efficient and reliable. These cyber-physical systems (CPS) now face an increasing threat of cyber-attacks. Intelligent attackers can leverage their knowledge of the system, disruption, and disclosure resources to critically damage the system while remaining undiscovered. In this dissertation, we develop a defense strategy, with the ability to uncover malicious and intelligent attacks and enable resilient operation of cyber-physical systems. Specifically, we apply this defense strategy to power systems, described by linear frequency dynamics around the nominal operating point. Our methodology is based on the notion of data aggregation as a tool for extracting internal information about the system that may be unknown to the attacker. As the first step to resilience and security, we propose several methods for active attack detection in cyber-physical systems. In one approach we design a clustering-based moving-target active detection algorithm and evaluate it against stealthy attacks on the 5-bus and 24-bus power grids. Next, we consider an approach based on Interaction Variables (IntVar), as another intuitive way to extract internal information in power grids. We evaluate the eectiveness of this approach on Automatic Generation Control (AGC), a vital control mechanism in today’s power grid. After an attack has been detected, mitigation procedures must be put in place to allow continued reliable operation or graceful degradation of the power grid. To that end, we develop a resilient state estimation algorithm, that provides the system operator with situational awareness in the presence of wide-spread coordinated cyber-attacks when many system measurements may become unavailable.
In an uncertain and complex environment, to ensure secure and stable operations of large-scale power systems is one of the biggest challenges that power engineers have to address today. Traditionally, power system operations and decision-making in controls are based on power system computations of physical models describing the behavior of power systems. Largely, physical models are constructed according to some assumptions and simplifications, and such is the case with power system models. However, the complexity of power system stability problems, along with the system's inherent uncertainties and nonlinearities, can result in models that are impractical or inaccurate. This calls for adaptive or deep-learning algorithms to significantly improve current control schemes that solve decision and control problems. Cyberphysical Infrastructures in Power Systems: Architectures and Vulnerabilities provides an extensive overview of CPS concepts and infrastructures in power systems with a focus on the current state-of-the-art research in this field. Detailed classifications are pursued highlighting existing solutions, problems, and developments in this area. Gathers the theoretical preliminaries and fundamental issues related to CPS architectures. Provides coherent results in adopting control and communication methodologies to critically examine problems in various units within smart power systems and microgrid systems. Presents advanced analysis under cyberphysical attacks and develops resilient control strategies to guarantee safe operation at various power levels.
In an uncertain and complex environment, to ensure secure and stable operations of large-scale power systems is one of the biggest challenges that power engineers have to address today. Traditionally, power system operations and decision-making in controls are based on power system computations of physical models describing the behavior of power systems. Largely, physical models are constructed according to some assumptions and simplifications, and such is the case with power system models. However, the complexity of power system stability problems, along with the system's inherent uncertainties and nonlinearities, can result in models that are impractical or inaccurate. This calls for adaptive or deep-learning algorithms to significantly improve current control schemes that solve decision and control problems. Cyberphysical Infrastructures in Power Systems: Architectures and Vulnerabilities provides an extensive overview of CPS concepts and infrastructures in power systems with a focus on the current state-of-the-art research in this field. Detailed classifications are pursued highlighting existing solutions, problems, and developments in this area. Gathers the theoretical preliminaries and fundamental issues related to CPS architectures. Provides coherent results in adopting control and communication methodologies to critically examine problems in various units within smart power systems and microgrid systems. Presents advanced analysis under cyberphysical attacks and develops resilient control strategies to guarantee safe operation at various power levels.
A cyber-physical system (CPS) is a tight coupling of computational resources, network communication, and physical processes. They are composed of a set of networked components, including sensors, actuators, control processing units, and communication agents that instrument the physical world to make ,Äúsmarter.,Äù However, cyber components are also the source of new, unprecedented vulnerabilities to malicious attacks. In order to protect a CPS from attacks, three security levels of protection, detection, and identification are considered. In this chapter, we will discuss the identification level, i.e., secure state estimation and attack reconstruction of CPS with corrupted states and measurements. Considering different attack plans that may assault the states, sensors, or both of them, different online attack reconstruction approaches are discussed. Fixed-gain and adaptive-gain finite-time convergent observation algorithms, specifically sliding mode observers, are applied to online reconstruction of sensor and state attacks. Next, the corrupted measurements and states are to be cleaned up online in order to stop the attack propagation to the CPS via the control signal. The proposed methodologies are applied to an electric power network, whose states and sensors are under attack. Simulation results illustrate the efficacy of the proposed observers.