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The objective of the research is to formulate and demonstrate protection schemes for radial and loop systems, an active distribution system, and a microgrid. The schemes are composed of a) A new loop scheme by utilizing voltage, current, and time (VIT) reclosers and sectionalizers and b) A new protection scheme, the dynamic state estimation-based protection, for active distribution systems and microgrids. The first part of the research explores the closing onto a fault during the conventional loop sectionalizing scheme and provides a VIT scheme that can solve the problem. The immediate benefit of the VIT schemes is a reduction of the nuisance trips because of the fault closing onto a fault. Moreover, the number of protection zones is increased by the application of the VIT sectionalizers. This thesis demonstrates the VIT protection scheme for a traditional distribution system and presents numerical experiments using various test scenarios with various fault locations. The simulation results verify that the protection scheme successfully performs the automatic load transfer scheme for a loop system. The second part of the research identifies the increased number of protection issues according to the installation of distributed generations (DGs) and provides solution to the problem. To solve the issue, a new fault detection scheme, dynamic state estimation-based protection scheme, is illustrated in this thesis based on synchronized measurements. The method uses dynamic state estimation, based on the dynamic model of the component that accurately reflects the nonlinear characteristics of the component. Numerical experiments show that the protection of active distribution systems and microgrids is feasible in real time.

Previously, conventional state estimation techniques have been used for state estimation in power systems. These conventional methods are based on steady state models. As a result of this, power system dynamics during disturbances or transient conditions are not adequately captured. This makes it challenging for operators in control centers to perform visual tracking of the system, proper fault diagnosis and even take adequate preemtive control measures to ensure system stability during voltage dips. Another challenge is that power systems are nonlinear in nature. There are multiple power components in operation at any given time making the system highly dynamic in nature. Consequently, the need to study and implement better dynamic estimation tools that capture system dynamics during disturbances and transient conditions is necessary. For this thesis work, we present the Unscented Kalman Filter (UKF) which integrates Unscented Transformation (UT) to Kalman Filtering. Our algorithm takes as input the output of a synchronous machine modeled in MATLAB/Simulink as well as data from a PMU device assumed to be installed at the terminal bus of the synchronous machine, and estimate the dynamic states of the system using a Kalman Filter. We have presented a detailed and analytical study of our proposed algorithm in estimating two dynamic states of the synchronous machine, rotor angle and rotor speed. Our study and result shows that our proposed methodology has better efficiency when compared to the results of the Extended Kalman Filter (EKF) algorithm in estimating dynamic states of a power system.  Our results are presented and analyzed on the basis of how accurately the algorithm estimates the system states following various simulated transient and small-signal disturbances.

Soft sensors are an essential component of process systems engineering schemes. While soft sensor design research is important, investigation into the relationships between soft sensors and other areas of advanced monitoring and control is crucial as well. This dissertation presents two new techniques that enhance the performance of fault detection and sensor network design by integration with soft sensor technology. In addition, a chapter is devoted to the investigation of the proper implementation of one of the most often used soft sensors. The performance advantages of these techniques are illustrated with several cases studies. First, a new approach for fault detection which involves soft sensors for process monitoring is developed. The methodology presented here deals directly with the state estimates that need to be monitored. The advantage of such an approach is that the nonlinear effect of abnormal process conditions on the state variables can be directly observed. The presented technique involves a general framework for using soft sensor design and computation of the statistics that represent normal operating conditions. Second, a method for determining the optimal placement of multiple sensors for processes described by a class of nonlinear dynamic systems is described. This approach is based upon maximizing a criterion, i.e., the determinant, applied to the empirical observability gramian in order to optimize certain properties of the process state estimates. The determinant directly accounts for redundancy of information, however, the resulting optimization problem is nontrivial to solve as it is a mixed integer nonlinear programming problem. This paper also presents a decomposition of the optimization problem such that the formulated sensor placement problem can be solved quickly and accurately on a desktop PC. Many comparative studies, often based upon simulation results, between Extended Kalman filters (EKF) and other estimation methodologies such as Moving Horizon Estimation or Unscented Kalman Filter have been published over the last few years. However, the results returned by the EKF are affected by the algorithm used for its implementation and some implementations may lead to inaccurate results. In order to address this point, this work provides a comparison of several different algorithms for implementation.

Within the field of Quantum Information Processing, we study two subjects: For quantum state tomography, one common assumption is that the experimentalist possesses a stationary source of identical states. We challenge this assumption and propose a method to detect and characterize the drift of nonstationary quantum sources. We distinguish diffusive and systematic drifts and examine how quickly one can determine that a source is drifting. Finally, we give an implementation of this proposed measurement for single photons. For quantum computing, fault-tolerant protocols assume that errors are of certain types. But how do we detect errors of the wrong type? The problem is that for large quantum states, a full state description is impossible to analyze, and so one cannot detect all types of errors. We show through a quantum state estimation example (on up to 25 qubits) how to attack this problem using model selection. We use, in particular, the Akaike Information Criterion. Our example indicates that the number of measurements that one has to perform before noticing errors of the wrong type scales polynomially both with the number of qubits and with the error size. This dissertation includes previously published co-authored material.

[pt] Vários trabalhos têm sido apresentados nos últimos anos abordando o problema de estimação de estado em sistemas de potência. Diferentes tipos de algoritmos - estáticos, rastreador e dinâmico - têm sido propostos junto com uma variedade de aplicações: detecção e identificação de erros grosseiros configurador de rede, etc. praticamente todos esses estudos foram efetuados para uma rede essencialmente de corrente alternada (CA). A presença de elos de corrente contínua (CC) em operação em vários sistemas de potência e a possibilidade futura da existência de redes, CC, fazem com que as técnicas de estimação de estado sejam estendidas para cobrir tais casos. Somente alguns trabalhos foram publicados até então abordando este tópico. O presente trabalho descreve um novo, simples e eficiente método de estimação estática de estado para sistemas mistos CA/CC. Basicamente o processo de estimação é feito de forma independente para as redes CA e CC, e posteriormente para a interconexão CA/CC. Tal característica faz com que o método proposto possa ser facilmente implementado em qualquer algoritmo de estimação de estado CA existente. O algoritmo proposto é aplicado a um sistema CC multiterminal integrado a um sistema CA e os resultados são discutidos. / [en] In the past few years, many papers have been published on state estimation applied to electric power systems. Different types of algorthms - static, tracking and dynamic - have been proposed aside with a variety of applications: detection and identification of bad data, networ configurator, etc. Most of such studies have been carried out for AC systems although there are some power systems around the world with DC links in operation. Moreover, engineers are now considering DC multiterminal networks as a feasible option. Therefore the state estimation techniques have to be extended to deal with such mixed AC/DC systems. So far, few papers have been published on this matter. This paper describes a new, simple and efficient method for the static state estimation of integrated HVDC/AC systems. Basically, the estimation process is carried out independently for the AC and DC networks, and then for the AC/DC interconnection system. Owing to the characteristics of the proposed method, it can be coupled to any existing AC state estimation algorithm. The proposed algorithm is applied to an integrated multiterminal DC/AC test systems and the results are discussed.

abstract: The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation through state estimation (SE), controlling the system to operate reliably, and optimizing the system operation efficiency. The SCADA acquires the noisy measurements, such as voltage angle and magnitude, line power flows, and line current magnitude, from the remote terminal units (RTUs). These raw data are firstly sent to the SE, which filters all the noisy data and derives the best estimate of the system state. Then the estimated states are used for other EMS functions, such as contingency analysis, optimal power flow, etc. In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks. Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015

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Wide-area monitoring for the power system is a key tool for preventing the power system from system wide failure. State Estimation (SE) is an essential and practical monitoring tool that has been widely used to provide estimated values for each quantity within energy management systems (EMS) in the control center. However, monitoring larger power systems coordinated by regional transmission operators has placed an enormous operational burden on current SE techniques. A distributed state estimation (DSE) algorithm with a hierarchical structure designed for the power system industry is much more computationally efficient and robust especially for monitoring a wide-area power system. Moreover, considering the deregulation of the power system industry, this method does not require sensitive data exchange between smaller areas that may be competing entities. The use of phasor measurement units (PMUs) in the SE algorithm has proven to improve the performance in terms of accuracy and converging speed. Being able to synchronize the measurements between different areas, PMUs are perfectly suited for distributed state estimation. This dissertation investigates the benefits of the DSE using PMU over a serial state estimator in wide area monitoring. A new method has been developed using available PMU data to calculate the reference angle differences between decomposed power systems in various situations, such as when the specific PMU data of the global slack bus cannot be obtained. The algorithms were tested on six bus, I standard 30 bus and I 118-bus test cases. The proposed distributed state estimator has also been implemented in a test bed to work with a power system real-time digital simulator (RTDS) that simulates the physical power system. PMUs made by SEL and GE are used to provide real-time inputs to the distributed state estimator. Simulation results demonstrated the benefits of the PMU and distributed SE techniques. Additionally a constructed test bed verified and validated the proposed algorithms and can be used for different smart grid tests.