Data Analytics and Wide-Area Visualization Associated with Power Systems Using Phasor Measurements

As power system research becomes more data-driven, this study presents a framework for the analysis and visualization of phasor measurement unit (PMU) data obtained from large, interconnected systems. The proposed framework has been implemented in three steps: (a) large-scale, synthetic PMU data generation: conducted to generate research-based measurements with the inclusion of features associated with industry-grade PMU data; (b) error and event detection: conducted to assess risk levels and data accuracy of phasor measurements, and furthermore search for system events or disturbances; (c) oscillation mode visualization: conducted to present wide-area, modal information associated with large-scale power grids. To address the challenges due to real data confidentiality, the creation of realistic, synthetic PMU measurements is proposed for research use. First, data error propagation models are generated after a study of some of the issues associated with the unique time-synchronization feature of PMUs. An analysis of some of the features of real PMU data is performed to extract some of the statistics associated with data errors. Afterwards, an approach which leverages on existing, large-scale, synthetic networks to model the constantly-changing dynamics often observed in real measurements is used to generate an initial synthetic dataset. Further inclusion of PMU-related data anomalies ensures the production of realistic, synthetic measurements fit for research purposes. An application of different techniques based on a moving-window approach is suggested for use in the detection of events in real and synthetic PMU measurements. These fast methods rely on smaller time-windows to assess fewer measurement samples for events, classify disturbances into global or local events, and detect unreliable measurement sources. For large-scale power grids with complex dynamics, a distributed error analysis is proposed for the isolation of local dynamics prior any reliability assessment of PMU-obtained measurements. Finally, fundamental system dynamics which are inherent in complex, interconnected power systems are made apparent through a wide-area visualization of large-scale, electric grid oscillation modes. The approach ensures a holistic interpretation of modal information given that large amounts of modal data are often generated in these complex systems irrespective of the technique that is used

Security and Statistics on Power Grids

Improving the functioning and the safety of the electrical grids is a topic of great concern, given its magnitude and importance in today's world. In this thesis, we focus in these two subjects. In the first part, we study undetectable cyber-physical attacks on power grids, which are attacks that involve physical disruptions, including tripping lines and load modifications, and sensor output alterations. We propose a sophisticated attack model described under the full Alternating Current (AC) power flow equations and show its feasibility on large grids from a test cases library. As counter-measures, we propose different defensive strategies that the network's controller can apply under a suspected cyber attack. These are random, simple and fast procedures that change the voltages across the network and aim to unmask the current status of the system, assuming that the attacker cannot react against their randomness. Secondly, with access to data collected through Phasor Measurement Units (PMUs) by a power utility in the United States, we perform statistical analyses on the frequency and voltage time series that have been recorded at a rate of 30 Hz. We focus on intervals of time where the sampled data shows to be in steady-state conditions and, with the use of appropriate signal processing filters, we are able to extract hidden anomalies such as spatio-temporal correlations between sensors and harmonic distortions

Machine learning approach for dynamic event identification in power systems with wide area measurement systems

Orientador: Prof. Dr. Alexandre Rasi AokiCoorientador: Prof. Dr. Ricardo SchumacherDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa : Curitiba, 13/02/2023Inclui referênciasResumo: Ao longo dos últimos dez anos, a disponibilidade de WAMS (Wide Area Measurement Systems) tem constantemente aumentado e, com isso, a necessidade de se otimizar seu uso em relação a uma ampla gama de capacidades requeridas nos centros de operação. Concorrentemente, o sistema brasileiro tem observado diversos eventos em múltiplos níveis de criticalidade e, portanto, formas de rapidamente identificar irregularidades na rede elétrica têm sido requisitadas pelos operadores. Todavia, mesmo com tal diversidade de eventos registrados por PMUs (Phasor Measurement Unit), há dificuldades em se consolidar um banco de dados de eventos e, ademais, sistemas diferem uns dos outros - isto é, os volumes de dados requeridos para machine learning e a especificidade de cada sistema criam desafios para a construção de aplicações para detecção e identificação de eventos em uma dada rede. De tal maneira, o presente trabalho propõe uma forma de endereçar tais restrições e habilitar o uso de modelos de machine learning na vida real ao modelar um sistema real, simular uma grande quantia de eventos (como medição de PMU) e executar o processo de aprendizado de máquina com esses dados simulados. Tendo posse de qualquer conjunto de dados que contenha medições de evento da mesma PMU simulada, uma validação da aplicabilidade e performance do modelo obtido pode ser feita. Assim, um processo reprodutível e escalável foi definido pelo trabalho a partir de um estudo de caso no corredor Salto Caxias, um subsistema da rede elétrica paranaense operado pela COPEL, que forneceu três conjuntos de dados contendo eventos registrados em uma PMU dessa área. Alguns componentes como barras, linhas de transmissão, transformadores, geradores, PSSs, excitadores e controles de turbina foram modelados dentro da Power System Toolbox, embasada em MATLAB, para simulação de eventos. O algoritmo de machine learning selecionado para provar o conceito estabelecido foi rede neural artificial, definindo-se quatro classes possíveis para reconhecimento - "Curto-circuito", "Perda de Carga", "Perda de Linha" e "Normal". Com o modelo de machine learning definido e treinado, se aplicaram os dados de eventos reais nele. Os resultados mostram que as métricas da rede neural no processo de aprendizado foram geralmente suficientes para aplicação em vida real, mas que sua performance nos conjuntos de dados de eventos reais foi abaixo da registrada com os dados simulados. Todavia, considerando-se que os dados reais providenciados são de eventos longínquos à PMU observada e ao próprio sistema modelado, distorções e atenuações de sinal são inerentes. Assim, pode-se dizer que o método proposto é aplicável, com mais etapas de pré-processamento de dados, a qualquer dado sistema - caso ele seja minuciosamente modelado e haja disponibilidade de conjuntos de dados de eventos internos ao sistema.Abstract: Over the last ten years, the availability of WAMS (Wide Area Measurement Systems) has steadily increased and, with it, the need to optimize its usage concerning a large array of capabilities required at the operation centers. Concurrently, the Brazilian system has witnessed various events at multiple levels of criticality, and, thus, ways to quickly identify irregularities in the grid have been more and more requested by power transmission and distribution companies. The introduction of machine learning models and algorithms in such a context has been explored by the scientific community. However, even with such a diversity of events and their PMU (Phasor Measurement Unit) measurements, there is hardship in consolidating an event database and systems differ from each other - that is, the data volume required for machine learning and the specificity of each power system create challenges in constructing applications for detection and identification of events in a given grid. As such, the present work proposes a way to address those constraints and further enable the real-life application of machine learning models in a power system with WAMS through the modeling of a real-life system, simulating a large database of events as if they were registered through a PMU in said system and training machine learning models on this simulated data. If one has any dataset containing event measurements from the same PMU (which was simulated), a validation of model performance and applicability can be performed. A reproducible and scalable process was defined to achieve this through one case study for the Salto Caxias subsystem of the Paraná state grid, operated by COPEL, who provided the author with three event datasets captured from a PMU in the aforementioned system. Some components of the system were modeled in MATLAB-based Power System Toolbox for dynamic simulation, such as generators, PSSs, exciters, and turbine governors in addition to buses, transmission lines, and transformers. The selected algorithm for this proof-of-concept was artificial neural network, defining four distinct possible classes it can recognize - "Short-circuit", "Load Loss", "Line Loss" and "Normal". With the machine learning model defined and trained, its application was executed on real event datasets. The results show that the metrics of the neural network model on the learning process were generally sufficient for real-life solutions, but its performance on the real event datasets was below that of the performance on simulated data. However, considering that the provided datasets were from events that happened far away from the selected PMU and its modeled system, signal distortions and attenuations are present. Thus, it can be stated that the proposed method is applicable, with further data preprocessing, to any given system - as long as it is thoroughly modeled and there is availability of datasets of events that happened within it

Design, analysis, and integration of a turboelectric propulsion and power system for unmanned aircraft

Electrically powered small unmanned aerial systems face limited range and endurance due to the inherently low energy density of current level of technology batteries. Hybrid gas-electric systems are under development and show promising signs of increased range and endurance, however piston based hybrid systems must overcome several challenges to be effective. Turboelectric power systems leverage the high energy density of hydrocarbon fuels in conjunction with the high power density of electric propulsion systems while mitigating some of the typical issues encountered with piston-based hybrid systems. In this study, a 7.3kW turboelectric power system for a small unmanned aircraft is realized through several phases of design, including an airframe integration phase. Though previous literature suggests a range of anticipated electrical efficiencies, the driving factoring contributing to power system losses are not well discussed. Thus, there exists a critical need to develop and validate a design approach that assesses compatibility of components of the electrical system and how component selection affects efficiency. Several overall system models were developed and evaluated for a general turboelectric power system over a range of operating conditions. System level design implications is discussed in detail, enabling optimization of the components during the design phase. An active throttle controller was designed, realized, and evaluated for the system described. The function of the throttle controller ranges from starting and stopping of the turbine to regulation of turbine throttle via feedback control. Switching between power sources and the difficulties associated with this will also be discussed in detail. The turboelectric system was installed on a Mugin 4500 fixed-wing unmanned aerial system and evaluated in terms of thermal management and operability. Results of this study will demonstrate feasibility of turboelectric power systems as an alternative to traditional all-electric propulsion systems and serve as a stepping-stone for future studies on small-scale turboelectric propulsion and power

Beneficiary country ownership and the politics of partnership in trilateral development cooperation: a case study of Zambia

Global discourses on trilateral development cooperation (TDC) have overlooked the experiences of beneficiary countries and focused on the concerns of development cooperation providers. This is a significant gap given that TDC is increasingly being promoted as a modality that supports country ownership, equality between Northern and Southern partners, and efforts to achieve the 2030 agenda for Sustainable Development. In response, this study draws on the case of Zambia to examine how the politics of partnership affect a beneficiary country’s experience with exercising ownership and leadership of TDC projects. It employs an institutional ethnography based on key stakeholder interviews and archival analysis, to capture the beneficiary perspective of country ownership and partnerships. It also engages with postcolonial perspectives on development cooperation to gain insight into how power and agency operate in the production and dissemination of development knowledge. The study finds that Zambian approaches to country ownership in TDC differ from definitions in global policy frameworks and reflect institutionalised responses to the experiences of colonial governance and donor dominance. This demonstrates the significance of a more nuanced understanding of beneficiary agency and the historical context of partnerships. The study also demonstrates that TDC is intertwined with the geopolitical and commercial interests of partner countries, although the dominant policy narratives prefer to concentrate on the technical aspects of project management. It also illustrates the diverse ways in which Zambian stakeholders navigate these challenges and concludes that a beneficiary country can achieve real and observable development outcomes from TDC, despite the politics of partnership. However, it argues that Zambia’s ability to ensure the sustainability of development outcomes are constrained by internal dynamics, rather than the underlying ambitions or power inequalities with its development cooperation providers. The findings contribute fresh insight into debates on the changing geographies of global development and emerging literature on the politics of knowledge production in South-South/trilateral cooperation research

Batteries and Supercapacitors Aging

Electrochemical energy storage is a key element of systems in a wide range of sectors, such as electro-mobility, portable devices, and renewable energy. The energy storage systems (ESSs) considered here are batteries, supercapacitors, and hybrid components such as lithium-ion capacitors. The durability of ESSs determines the total cost of ownership, the global impacts (lifecycle) on a large portion of these applications and, thus, their viability. Understanding ESS aging is a key to optimizing their design and usability in terms of their intended applications. Knowledge of ESS aging is also essential to improve their dependability (reliability, availability, maintainability, and safety). This Special Issue includes 12 research papers and 1 review article focusing on battery, supercapacitor, and hybrid capacitor aging

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Achievements of Hinode in the first eleven years

This is the final version. Available on open access from OUP via the DOI in this recordHinode is Japan's third solar mission following Hinotori (19811982) and Yohkoh (19912001) launched on 2006 September 22 and is in operation currently. Hinode carries three instruments; the Solar Optical Telescope (SOT), the X-Ray Telescope (XRT), and the EUV Imaging Spectrometer (EIS). These instruments were built under international collaboration with NASA and STFC (UK), and its operation has been contributed by ESA and NSC (Norway). After describing satellite operations and performance evaluation of the three instruments, reviews are given on major scientific discoveries by Hinode in the first ten years of its operation. Future prospects on solar physics research based on the achievements of Hinode conclude this review article.Science and Technology Facilities Counci