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A Comprehensive Approach to Implement Monitoring and State Estimation in Distribution Grids with a Low Number of Measurements
This work addresses the monitoring and state estimation of electrical grids, especially at the distribution level. For economic and technical reasons, grid monitoring cannot be implemented with a similarly high measurement density as in transmission grids. Two new monitoring methods, which are designed for low measurement density, are therefore presented for use in real-time grid operation. First, a heuristic monitoring method is presented, which does not require pseudo-measurements and estimates voltage magnitudes and line loadings. Second, a monitoring method based on artificial neural networks is presented. With appropriate training, the method can estimate grid variables, e.g., voltage magnitudes or line loadings, with high accuracy. The methods are tested on thousands of test scenarios using a comprehensive evaluation methodology. For measurement infrastructure planning, a concept is presented to determine suitable measurement locations for the use of one of the monitoring methods. After optimization, several possible measurement configurations are presented with their average and maximum errors and the projected capital expenditures.
Artificial Intelligence Techniques for a Scalable Energy Transition
This book presents research in artificial techniques using intelligence for energy transition, outlining several applications including production systems, energy production, energy distribution, energy management, renewable energy production, cyber security, industry 4.0 and internet of things etc. The book goes beyond standard application by placing a specific focus on the use of AI techniques to address the challenges related to the different applications and topics of energy transition. The contributions are classified according to the market and actor interactions (service providers, manufacturers, customers, integrators, utilities etc.), to the SG architecture model (physical layer, infrastructure layer, and business layer), to the digital twin of SG (business model, operational model, fault/transient model, and asset model), and to the application domain (demand side management, load monitoring, micro grids, energy consulting (residents, utilities), energy saving, dynamic pricing revenue management and smart meters, etc.).
Structural Optimizations in Strategic Medium Voltage Power System Planning
This thesis introduces a comprehensive methodology for the automation of the strategic power system planning process in the medium voltage level. The methodology takes the predicted development of load and distributed generation as well as the age structure of the components into account. Target grid structures are computed with a heuristic search that considers constraints for the grid topology, power flow parameters in normal as well as contingency operation, fault currents and service reliability. The implementation is based on the newly presented open source power systems analysis tool pandapower, which allows grid modelling and analysis with a high degree of automation. The developed methodology is applied to three real case study grids from different power system operators. The structural optimization leads to a reduction of investment and operational costs within the planning horizon of up to 56% in the target grids compared to the present grid structures. The successful application of the developed method to a diverse set of case studies demonstrates its general applicability in realistic planning problems.
Transitioning to Affordable and Clean Energy
Transitioning to Affordable and Clean Energy is a collective volume which combines original contributions and review papers that address the question how the transition to clean and affordable energy can be governed. It will cover both general analyses of the governance of transition, including policy instruments, comparative studies of countries or policies, and papers setting out scientifically sound visions of a clean and just energy system. In particular, the following aspects are foregrounded: • Governing the supply and demand side transformation • Geographical and cultural differences and their consequences for the governance of energy transitions • Sustainability and justice rel...
Application of Data Mining and Machine Learning Methods to Industrial Heat Treatment Processes for Hardness Prediction
This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework. - This work presents a data mining framework applied to industrial heattreatment (bainitization and case hardening) aiming to optimize processes and reduce costs. The framework analyses factors such as material, production line, and quality assessment for preprocessing, feature extraction, and drift corrections. Machine learning is employed to devise robust prediction strategies for hardness. Its implementation in an industry pilot demonstrates the economic benefits of the framework.
Development of a MATLAB/Simulink Framework for Phasor-Based Power System Simulation and Component Modeling Based on State Machines
Im ersten Teil dieser Arbeit wird ein Algorithmus vorgestellt, der spannungsabhängige Einspeisung von Wirk- und Blindleistung in den Lastfluss-Algorithmus integriert. Es wird eine Beschleunigung von bis zu einer Größenordnung gegenüber dem derzeit gängigen Verfahren, und eine verbesserte Robustheit erreicht. Im zweiten Teil wird ein Phasor-Framework zur dynamischen Simulation von Stromnetzen vorgestellt. Die wesentliche Neuheit ist die Möglichkeit der Integration von Zustandsdiagrammen direkt in die Komponentenmodelle. Damit wird eine wesentlich schnellere Modellentwicklung ermöglicht als mit verfügbaren Tools. Im dritten Teil werden Modelle entwickelt und in das Framework integriert. Der Schwerpunkt liegt auf einem Photovoltaik-Modell welches das dynamische P(V), Q(V) und P(f) Verhalten nach VDE 4105 im Bereich Sekunden bis Minuten abbildet. Im vierten Teil wird das entwickelte Phasor-Framework verwendet, um das Wiederzuschaltverhalten von Photovoltaikanlagen in einem dieselbetriebenen Inselnetz in der Niederspannung zu untersuchen. Die Untersuchung zeigt, dass ein periodisches Ab- und Abschalten von Photovoltaikanlagen vorkommen kann.
Evolutionary Computing and Mobile Sustainable Networks
This book features selected research papers presented at the International Conference on Evolutionary Computing and Mobile Sustainable Networks (ICECMSN 2020), held at the Sir M. Visvesvaraya Institute of Technology on 20–21 February 2020. Discussing advances in evolutionary computing technologies, including swarm intelligence algorithms and other evolutionary algorithm paradigms which are emerging as widely accepted descriptors for mobile sustainable networks virtualization, optimization and automation, this book is a valuable resource for researchers in the field of evolutionary computing and mobile sustainable networks.
Jan Hendrik Oort
This book is the first thorough and overdue biography of one of the giants of science in the twentieth century, Jan Hendrik Oort. His fundamental contributions had a lasting effect on the development of our insight and a profound influence on the international organization and cooperation in his area of science and on the efforts and contribution of his native country. This book aims at describing Oort's life and works in the context of the development of his branch of science and as a tribute to a great scientist in a broader sense. The astronomer Jan Hendrik Oort from the Netherlands was founder of studies of the structure and dynamics of the Milky Way Galaxy, initiator of radioastronomy and the European Southern Observatory, and an important contributor to many areas of astronomy, from the study of comets to the universe on the largest scales.
Commanders Digest
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