THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

THE STABILITY ANALYSIS FOR WIND TURBINES WITH DOUBLY FED INDUCTION GENERATORS

The quickly increasing, widespread use of wind generation around the world reduces carbon emissions, decreases the effects of global warming, and lowers dependence on fossil fuels. However, the growing penetration of wind power requires more effort to maintain power systems stability. This dissertation focuses on developing a novel algorithm which dynamically optimizes the proportional-integral (PI) controllers of a doubly fed induction generator (DFIG) driven by a wind turbine to increase the transient performance based on small signal stability analysis. Firstly, the impact of wind generation is introduced. The stability of power systems with wind generation is described, including the different wind generator technologies, and the challenges in high wind penetration conditions. Secondly, the small signal stability analysis model of wind turbines with DFIG is developed, including detailed rotor/grid side converter models, and the interface with the power grid. Thirdly, Particle swarm optimization (PSO) is selected to off-line calculate the optimal parameters of DFIG PI gains to maximize the damping ratios of system eigenvalues in different wind speeds. Based on the historical data, the artificial neural networks (ANNs) are designed, trained, and have the ability to quickly forecast the optimal parameters. The ANN controllers are designed to dynamically adjust PI gains online. Finally, system studies have been provided for a single machine connected to an infinite bus system (SMIB), a single machine connected to a weak grid (SMWG), and a multi machine system (MMS), respectively. A detailed analysis for MMS with different wind penetration levels has been shown according to grid code. Moreover, voltage stability improvement and grid loss reduction in IEEE 34-bus distribution system, including WT-DFIG under unbalanced heavy loading conditions, are investigated. The simulation results show the algorithm can greatly reduce low frequency oscillations and improve transient performance of DFIGs system. It realizes off-line optimization of MMS, online forecasts the optimal PI gains, and adaptively adjusts PI gains. The results also provide some useful conclusions and explorations for wind generation design, operations, and connection to the power grid. Advisors: Sohrab Asgarpoor and Wei Qia

Innovative Wireless Localization Techniques and Applications

Innovative methodologies for the wireless localization of users and related applications are addressed in this thesis. In last years, the widespread diffusion of pervasive wireless communication (e.g., Wi-Fi) and global localization services (e.g., GPS) has boosted the interest and the research on location information and services. Location-aware applications are becoming fundamental to a growing number of consumers (e.g., navigation, advertising, seamless user interaction with smart places), private and public institutions in the fields of energy efficiency, security, safety, fleet management, emergency response. In this context, the position of the user - where is often more valuable for deploying services of interest than the identity of the user itself - who. In detail, opportunistic approaches based on the analysis of electromagnetic field indicators (i.e., received signal strength and channel state information) for the presence detection, the localization, the tracking and the posture recognition of cooperative and non-cooperative (device-free) users in indoor environments are proposed and validated in real world test sites. The methodologies are designed to exploit existing wireless infrastructures and commodity devices without any hardware modification. In outdoor environments, global positioning technologies are already available in commodity devices and vehicles, the research and knowledge transfer activities are actually focused on the design and validation of algorithms and systems devoted to support decision makers and operators for increasing efficiency, operations security, and management of large fleets as well as localized sensed information in order to gain situation awareness. In this field, a decision support system for emergency response and Civil Defense assets management (i.e., personnel and vehicles equipped with TETRA mobile radio) is described in terms of architecture and results of two-years of experimental validation

Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Device for measurement and control of humidity in crude oil and petroleum products

A device with a frequency-modulated output signal has been developed to increase the sensitivity and accuracy of measuring moisture content in crude oil and petroleum products in the range of 0~20%. The main element of the device is a self-oscillator transducer based on a transistor structure with negative differential resistance. A capacitive sensor in the form of a capacitive cylindrical structure with cylindrical electrodes was used to determine moisture content in crude oil and petroleum products. Electric permittivity of a two-component mixture of oil and water was estimated and the capacitance of the humidity-sensitive capacitive cylindrical structure with cylindrical electrodes was calculated. An electrical diagram of the device for measuring and controlling the humidity of crude oil and petroleum products has been developed. The relative error of converting the humidity of oil and petroleum products into capacitance which was caused by the change in oil temperature, was determined to be 0.225%. Values of relative errors of the device for measuring the humidity of oil and petroleum products are as follows: 1.355 · 10 -5% is caused by instability of the oscillator frequency, 0.01% is caused by fluctuations in the supply voltage of the self-oscillator transducer, 0.05% is caused by a change in ambient temperature by 1°C. For the developed device, which used errors of the first and second type, the reliability of humidity control of oil and petroleum products has been determined to be 0.9591

Run-time reconfiguration for efficient tracking of implanted magnets with a myokinetic control interface applied to robotic hands

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2021.Este trabalho introduz a aplicação de soluções de aprendizagem de máquinas visado ao problema do rastreamento de posição do antebraço baseado em sensores magnéticos. Especi ficamente, emprega-se uma estratégia baseada em dados para criar modelos matemáticos que possam traduzir as informações magnéticas medidas em entradas utilizáveis para dispositivos protéticos. Estes modelos são implementados em FPGAs usando operadores customizados de ponto flutuante para otimizar o consumo de hardware e energia, que são importantes em dispositivos embarcados. A arquitetura de hardware é proposta para ser implementada como um sistema com reconfiguração dinâmica parcial, reduzindo potencialmente a utilização de recursos e o consumo de energia da FPGA. A estratégia de dados proposta e sua implemen tação de hardware pode alcançar uma latência na ordem de microssegundos e baixo consumo de energia, o que encoraja mais pesquisas para melhorar os métodos aqui desenvolvidos para outras aplicações.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).This work introduces the application of embedded machine learning solutions for the problem of magnetic sensors-based limb tracking. Namely, we employ a data-driven strat egy to create mathematical models that can translate the magnetic information measured to usable inputs for prosthetic devices. These models are implemented in FPGAs using cus tomized floating-point operations to optimize hardware and energy consumption, which are important in wearable devices. The hardware architecture is proposed to be implemented as a dynamically partial reconfigured system, potentially reducing resource utilization and power consumption of the FPGA. The proposed data-driven strategy and its hardware implementa tion can achieve a latency in the order of microseconds and low energy consumption, which encourages further research on improving the methods herein devised for other application

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Earth Resources: A continuing bibliography with indexes, issue 40

This bibliography lists 423 reports, articles, and other documents introduced into the NASA scientific and technical information system between October 1 and December 31, 1983. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Computational Intelligence Techniques for Control and Optimization of Wastewater Treatment Plants

The development of novel, practice-oriented and reliable instrumentation and control strategies for wastewater treatment plants in order to improve energy efficiency, while guaranteeing process stability and maintenance of high cleaning capacity, has become a priority for WWTP operators due to increasing treatment costs. To achieve these ambitious and even contradictory objectives, this thesis investigates a combination of online measurement systems, computational intelligence and machine learning methods as well as dynamic simulation models. Introducing the state-of-the-art in the fields of WWTP operation, process monitoring and control, three novel computational intelligence enabled instrumentation, control and automation (ICA) methods are developed and presented. Furthermore, their potential for practical implementation is assessed. The methods are, on the one hand, the automated calibration of a simulation model for the Rospe WWTP that provides a basis for the development and evaluation of the subsequent methods, and on the other hand, the development of soft sensors for the WWTP inflow which estimate the crucial process variables COD and NH4-N, and the estimation of WWTP operating states using Self- Organising Maps (SOM) that are used to determine the optimal control parameters for each state. These collectively, provide the basis for achieving comprehensive WWTP optimization. Results show that energy consumption and cleaning capacity can be improved by more than 50%