Large scale outages have occurred worldwide in recent decades with some impacting 15-25% of a nation’s population. The complexity of blackouts has been extensively studied but many questions remain. As there are no perfect solutions to prevent blackouts, usually caused by a complex sequence of cascading events, a number of different measures need to be undertaken to minimize impact of future disturbances. Increase in deployment of phasor measurement units (PMUs) across the grid has given power industry an unprecedented technology to study dynamic behavior of the system in real time. Integration of large scale synchronized measurements with SCADA system requires a careful roadmap and methodology. When properly engineered, tested, and implemented, information extracted from synchrophasor data streams provides realtime observability for transmission system. Synchrophasor data can provide operators with quick insight into precursors of blackout (e.g., angular divergence) which are unavailable in traditional SCADA systems. Current visualization tools and SE functions, supported by SCADA, provide some basic monitoring. Inaccuracies in measurements and system models, absence of redundancy in the measured parameters or breaker statuses in most cases, and lack of synchronization and time resolution in SCADA data result in limited functionality and precision for a typical EMS required in today’s operating environment of tighter margins that require more frequent and more precise data. Addition of synchrophasor data, typically having several orders of magnitude higher temporal resolution, (i.e., 60 to 120 measurements per second as opposed to one measurement every 4 to 8 seconds), can help detect higher speed phenomena and system oscillations. Also, time synchronization to one micro-second allows for accurate comparison of phase angles across the grid and identification of major disturbances and islanding. This dissertation proposes a more comprehensive, holistic set of criteria for optimizing PMU placement with consideration for diverse factors that can influence PMU siting decision-making process and incorporates several practical implementation aspects. An innovative approach to interoperability testing is presented and solutions are offered to address the challenges. The proposed methodology is tested to prove the concept and address real-life implementation challenges, such as interoperability among the PMUs located across a large area.

Battery Electric Vehicles (BEV) are gaining market share but still must overcome several engineering challenges related to the lithium-ion battery packs powering them. The batteries must be carefully managed to optimize safety and performance. The estimation of battery states, which cannot be measured directly, is an important part of battery management and remains an active area of research since small gains in estimation accuracy can help reduce cost and increase BEV range. This thesis presents several improvements to battery state estimation using different methods. Electrochemical Impedance Spectroscopy (EIS) is receiving increased attention from researchers as a method for state estimation and diagnostics for real-time applications. Due to battery relaxation behaviour, long rest times are commonly used before performing the EIS measurement. In this work, methods were developed to significantly shorten the required rest times, and a State of Health (SoH) estimation strategy was proposed by taking advantage of the relaxation effect as measured by EIS. This method was demonstrated to have an estimation error of below 1%. At low temperatures, the accuracy of the battery model becomes poor due to the non-linear battery response to current. By using an adaptive filter called the Interacting Multiple Model (IMM) filter, the next part of this work showed how to significantly improve low temperature State of Charge (SoC) estimation. Further reduction in estimation errors was achieved by pairing the IMM with the Smooth Variable Structure Filter (SVSF), for SoC estimation errors below 2%. The work presented in this thesis also includes the application of Deep Neural Networks (DNN) for SoC estimation from EIS data. Finally, an extensive aging study was conducted and an accelerated protocol was compared to a realistic drive cycle based protocol using EIS as a characterization tool. / Thesis / Doctor of Philosophy (PhD) / Replacing conventional gasoline/diesel powered cars with battery powered vehicles is part of a solution to the climate crisis. However, the initial costs paired with range anxiety stops many from switching to electric cars. Both cost and range are related to the battery pack. To achieve the best possible range for the lowest possible cost, battery packs must be carefully controlled by sophisticated algorithms. Unfortunately, battery range or health cannot be measured directly, but must be inferred through measurable indicators. This thesis explores battery behavior under different operating conditions and develops improved methods which can be used to determine battery health and/or range. A powerful method usually used only in laboratory settings is studied and improved to make it more suitable for implementation in electric cars. In this work it is used for accurate battery health determination. Furthermore, a strategy for improving battery range determination at low temperatures is also proposed.

Kalman-type filtering methods are mostly designed based on exact knowledge of the system’s model with known parameters. In real applications, there may be considerable amount of uncertainties about the model structure, physical parameters, level of noise, and initial conditions. In order to overcome such difficulties, robust state estimation techniques are recommended. This PhD thesis presents a novel robust state estimation method that is referred to as the 2nd-order smooth variable structure filter (2nd-order SVSF) and satisfies the first and second order sliding conditions. It is an extension to the 1st-order SVSF introduced in 2007. In the 1st-order SVSF chattering is reduced by using a smoothing boundary layer; however, the 2nd-order SVSF alleviates chattering by preserving the second order sliding condition. It reduces the estimation error and its first difference until the existence boundary layer is reached. Then after, it guarantees that the estimation error and its difference remain bounded given bounded noise and modeling uncertainties. As such, the 2nd-order SVSF produces more accurate and smoother state estimates under highly uncertain conditions than the 1st-order version. The main issue with the 2nd-order SVSF is that it is not optimal in the mean square error sense. In order to overcome this issue, the dynamic 2nd-order SVSF is initially presented based on a dynamic sliding mode manifold. This manifold introduces a variable cut-off frequency coefficient that adjusts the filter bandwidth. An optimal derivation of the 2nd-order SVSF is then obtained by minimizing the state error covariance matrix with respect to the cut-off frequency matrix. An experimental setup of an electro-hydrostatic actuator is used to compare the performance of the 2nd-order SVSF and its optimal version with other estimation methods such as the Kalman filter and the 1st-order SVSF. Experiments confirm the superior performance of the 2nd-order SVSF given modeling uncertainties. / Thesis / Doctor of Philosophy (PhD)

Aircraft in the US are equipped with Emergency Locator Transmitters (ELTs). In emergency situations these beacons are activated, providing a radio signal that can be used to locate the aircraft. Recent developments in UAV technologies have enabled mini-UAVs (5-foot wingspan) to possess a high level of autonomy. Due to the small size of these aircraft they are human-packable and can be easily transported and deployed in the field. Using a custom-built Radio Direction Finder, we gathered readings from a known transmitter and used them to compare various Bayesian reasoning-based filtering algorithms. Using a custom-developed simulator, we were able to test and evaluate filtering and control methods. In most non-trivial conditions we found that the Sequential Importance Resampling (SIR) Particle Filter worked best. The filtering and control algorithms presented can be extended to other problems that involve UAV control and tracking with noisy non-linear sensor behavior.

Data that is represented with high dimensionality presents a computational complexity challenge for many existing algorithms. Limiting dimensionality by discarding attributes is sometimes a poor solution to this problem because significant high-level concepts may be encoded in the data across many or all of the attributes. Non-linear dimensionality reduction (NLDR) techniques have been successful with many problems at minimizing dimensionality while preserving intrinsic high-level concepts that are encoded with varying combinations of attributes. Unfortunately, many challenges remain with existing NLDR techniques, including excessive computational requirements, an inability to benefit from prior knowledge, and an inability to handle certain difficult conditions that occur in data with many real-world problems. Further, certain practical factors have limited advancement in NLDR, such as a lack of clarity regarding suitable applications for NLDR, and a general inavailability of efficient implementations of complex algorithms. This dissertation presents a collection of papers that advance the state of NLDR in each of these areas. Contributions of this dissertation include: • An NLDR algorithm, called Manifold Sculpting, that optimizes its solution using graduated optimization. This approach enables it to obtain better results than methods that only optimize an approximate problem. Additionally, Manifold Sculpting can benefit from prior knowledge about the problem. • An intelligent neighbor-finding technique called SAFFRON that improves the breadth of problems that existing NLDR techniques can handle. • A neighborhood refinement technique called CycleCut that further increases the robustness of existing NLDR techniques, and that can work in conjunction with SAFFRON to solve difficult problems. • Demonstrations of specific applications for NLDR techniques, including the estimation of state within dynamical systems, training of recurrent neural networks, and imputing missing values in data. • An open source toolkit containing each of the techniques described in this dissertation, as well as several existing NLDR algorithms, and other useful machine learning methods.

This thesis presents methods for robust precision landing of unmanned air vehicles (UAVs) on platforms at sea. Localization methods are proposed for UAV-to-boat state estimation for systems that employ real- time kinematic (RTK) global navigation satellite system (GNSS) and vision sensors. Solutions for GNSS-only are first presented, followed by the fusion of GNSS and vision. The important problem of sensor intrinsic calibration is solved with a novel offline batch estimation approach. Hardware results are presented for all methods. Our calibration of GNSS-to-camera is shown to estimate sensor offsets with millimeter level accuracy. Localization systems are combined with custom state machines that manage the landing attempt via a novel descent cone. This conical threshold enforces a safe and accurate landing. Our landing methods are demonstrated in real-world experiments and achieve consistent accurate landings with error below 10 cm. The fusion of camera and RTK is shown to produce a robust landing system with redundant localization sources.

Society is increasingly dependent on the proper functioning of electric power systems. Today's electric power systems rely heavily on information and networking technology in order to achieve efficient and secure operation. Recent initiatives to upgrade power systems into smart grids target an even tighter integration with information and communication technologies in order to enable the integration of renewable energy sources, local and bulk generation and demand response. Therefore for a proper functioning of smart grids, it is essential that the communication network is secure and reliable both in the face of network failures and in the face of attacks. This thesis contributes to improving the security of power system applications against attacks on the communication infrastructure. The contributions lie in two areas. The first area is the interaction of network and transport layer protocols with power system application layer security. We consider single and multi-area power system state estimation based on redundant telemetry measurements. The state estimation is a basis for a set of applications used for information support in the control center, and therefore its security is an important concern. For the case of single-area state estimation, we look at the security of measurement aggregation over a wide area communication network. Due to the size and complexity of power systems, it can be prohibitively expensive to introduce cryptographic security in every component of the communication infrastructure. Therefore, we investigate how the application layer logic can be leveraged to optimize the deployment of network, transport and application layer security solutions. We define security metrics that quantify the importance of particular components of the network infrastructure. We provide efficient algorithms to calculate the metrics, and that allow identification of the weakest points in the infrastructure that have to be secured. For the case of multi-area state estimation, we look at the security of data exchange between the control centers of neighboring areas. Although the data exchange is typically cryptographically secure, the communication infrastructure of a control center may get compromised by a targeted trojan that could attack the data before the cryptographic protection is applied or after it is removed. We define multiple attack strategies for which we show that they can significantly disturb the state estimation. We also show a possible way to detect and to mitigate the attack. The second area is a study of the communication availability at the application layer. Communication availability in power systems has to be achieved in the case of network failures as well as in the case of attacks. Availability is not necessarily achieved by cryptography, since traffic analysis attacks combined with targeted denial-of-service attacks could significantly disturb the communication. Therefore, we study how anonymity networks can be used to improve availability, which comes at the price of increased communication overhead and delay. Because of the way anonymity networks operate, one would expect that availability would be improved with more overhead and delay. We show that surprisingly this is not always the case. Moreover, we show that it is better to overestimate than to underestimate the attacker's capabilities when configuring anonymity networks. / <p>QC 20130522</p>

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Fault Detection and Diagnosis (FDD) constitutes an essential aspect of modern life, with far-reaching implications spanning various domains such as healthcare, maintenance of industrial machinery, and cybersecurity. A comprehensive approach to FDD entails addressing facets related to detection, invariance, isolation, identification, and supervision. In FDD, there are two main perspectives: model-based and data-driven approaches. This thesis centers on model-based methodologies, particularly within the context of control and industrial applications. It introduces novel estimation strategies aimed at enhancing computational efficiency, addressing fault discretization, and considering robustness in fault detection strategies. In cases where the system's behavior can vary over time, particularly in contexts like fault detection, presenting multiple scenarios is essential for accurately describing the system. This forms the underlying principle in Multiple Model Adaptive Estimation (MMAE) like well-established Interacting Multiple Model (IMM) strategy. In this research, an exploration of an efficient version of the IMM framework, named Updated IMM (UIMM), is conducted. UIMM is applied for the identification of irreversible faults, such as leakage and friction faults, within an Electro-Hydraulic Actuator (EHA). It reduces computational complexity and enhances fault detection and isolation, which is very important in real-time applications such as Fault-Tolerant Control Systems (FTCS). Employing robust estimation strategies such as the Smooth Variable Structure Filter (SVSF) in the filter bank of this algorithm will significantly enhance its performance, particularly in the presence of system uncertainties. To relax the irreversible assumption used in the UIMM algorithm and thereby expanding its application to a broader range of problems, the thesis introduces the Moving Window Interacting Multiple Model (MWIMM) algorithm. MWIMM enhances efficiency by focusing on a subset of possible models, making it particularly valuable for fault intensity and Remaining Useful Life (RUL) estimation. Additionally, this thesis delves into exploring chattering signals generated by the SVSF filter as potential indicators of system faults. Chattering, arising from model mismatch or faults, is analyzed for spectral content, enabling the identification of anomalies. The efficacy of this framework is verified through case studies, including the detection and measurement of leakage and friction faults in an Electro-Hydraulic Actuator (EHA). / Thesis / Candidate in Philosophy / In everyday life, from doctors diagnosing illnesses to mechanics inspecting cars, we encounter the need for fault detection and diagnosis (FDD). Advances in technology, like powerful computers and sensors, are making it possible to automate fault diagnosis processes and take corrective actions in real-time when something goes wrong. The first step in fault detection and diagnosis is to precisely identify system faults, ensuring they can be properly separated from normal variations caused by uncertainties, disruptions, and measurement errors. This thesis explores model-based approaches, which utilize prior knowledge about how a normal system behaves, to detect abnormalities or faults in the system. New algorithms are introduced to enhance the efficiency and flexibility of this process. Additionally, a new strategy is proposed for extracting information from a robust filter, when used for identifying faults in the system.

Real time monitoring of the shipboard power system is a complex task to address. Unlike the terrestrial power system, the shipboard power system is a comparatively smaller system but with more complexity in terms of its system operation. This requires the power system to be continuously monitored to detect any type of fluctuations or disturbances. Planning metering systems in the power system of a ship is a challenging task not only due to the dimensionality of the problem, but also due to the need for reducing redundancy while improving network observability and efficient data collection for a reliable state estimation process. This research is geared towards the use of a Genetic Algorithm for intelligent placement of meters in a shipboard system for real time power system monitoring taking into account different system topologies and critical parameters to be measured from the system. The algorithm predicts the type and location of meters for identification and collection of measurements from the system. The algorithm has been tested with several system topologies.