Uma breve revisão sobre métodos Meta-Heurísticos para a extração dos parâmetros Fotovoltaicos

As mudanças climáticas, o aumento da poluição e as crescentes preocupações ambientais colocam a humanidade diante de um problema energético. É nesse contexto que as energias renováveis assumem um papel fundamental para alcançar a neutralidade carbónica. Assim, para reduzir a utilização dos combustíveis fosseis é indispensável que as fontes de energia renovável se afirmem como uma solução vantajosa e viável para a produção de energia elétrica. Este aumento de produção de energia elétrica a partir de fontes renováveis é vital para se cumprirem os vários acordos mundiais e europeus que foram assinados com o propósito de atingir os desígnios assinados. A fonte de energia renovável com o maior potencial no futuro é a energia solar. No entanto, para esta energia se consolidar é necessário que as tecnologias fotovoltaicas sejam mais eficientes. A presente dissertação tem como objetivo analisar uma série de fatores que influenciam a determinação dos parâmetros e que caraterizam os respetivos modelos matemáticos. Concretamente, os fatores determinantes que foram analisados foram: os modelos matemáticos, as tecnologias PV, os métodos/algoritmos de otimização que foram utilizados para simular o comportamento de uma célula ou módulo fotovoltaico e, por último, a técnica aplicada para contornar a natureza implícita das equações que caraterizam o respetivo modelo fotovoltaico.Climate change, the increasing pollution, and growing environmental concerns place humanity in the face of an energetic problem. In this context, renewable energies play a key role in achieving carbon neutrality. Thus, in order to reduce the use of fossil fuels it is essential that renewable energy sources establish themselves as an advantageous and viable solution for the production of electricity. Increasing the production of electrical energy from renewable sources is crucial to meet the various global and European agreements that have been signed aiming the achievement of the proposed objectives. The renewable energy source with the highest potential for the future is solar energy. However, to consolidate this energy, photovoltaic technologies must be more efficient. The present dissertation aims to analyse a series of factors that influence the determination of the parameters that characterize the respective mathematical models. Specifically, the determining factors that have been analysed are: the mathematical models, the PV technologies, the optimization methods/algorithms that were used to simulate the behavior of a photovoltaic cell or module, and the technique applied to avoid the implicit nature of the equations that characterize the respective photovoltaic model

A Data-Driven Predictive Model of Reliability Estimation Using State-Space Stochastic Degradation Model

The concept of the Industrial Internet of Things (IIoT) provides the foundation to apply data-driven methodologies. The data-driven predictive models of reliability estimation can become a major tool in increasing the life of assets, lowering capital cost, and reducing operating and maintenance costs. Classical models of reliability assessment mainly rely on lifetime data. Failure data may not be easily obtainable for highly reliable assets. Furthermore, the collected historical lifetime data may not be able to accurately describe the behavior of the asset in a unique application or environment. Therefore, it is not an optimal approach anymore to conduct a reliability estimation based on classical models. Fortunately, most of the industrial assets have performance characteristics whose degradation or decay over the operating time can be related to their reliability estimates. The application of the degradation methods has been recently increasing due to their ability to keep track of the dynamic conditions of the system over time. The main purpose of this study is to develop a data-driven predictive model of reliability assessment based on real-time data using a state-space stochastic degradation model to predict the critical time for initiating maintenance actions in order to enhance the value and prolonging the life of assets. The new degradation model developed in this thesis is introducing a new mapping function for the General Path Model based on series of Gamma Processes degradation models in the state-space environment by considering Poisson distributed weights for each of the Gamma processes. The application of the developed algorithm is illustrated for the distributed electrical systems as a generic use case. A data-driven algorithm is developed in order to estimate the parameters of the new degradation model. Once the estimates of the parameters are available, distribution of the failure time, time-dependent distribution of the degradation, and reliability based on the current estimate of the degradation can be obtained

New Developments in Renewable Energy

Renewable energy is defined as the energy which naturally occurs, covers a number of sources and technologies at different stages, and is theoretically inexhaustible. Renewable energy sources such as those who are generated from sun or wind are the most readily-available and possible solutions to address the challenge of growing energy demands in the world. Newer and environmentally friendly technologies are able to provide different social and environmental benefits such as employment and decent environment. Renewable energy technologies are crucial contributors to world energy security, reduce reliance on fossil fuels, and provide opportunities for mitigating greenhouse gases. International public opinion indicates that there is strong support for a variety of methods for solving energy supply problems, one of which is utilizing renewable energy sources. In recent years, countries realized that that the renewable energy and its sector are key components for greener economies

Management: A bibliography for NASA managers

This bibliography lists 731 reports, articles and other documents introduced into the NASA Scientific and Technical Information System in 1990. Items are selected and grouped according to their usefulness to the manager as manager. Citations are grouped into ten subject categories: human factors and personnel issues; management theory and techniques; industrial management and manufacturing; robotics and expert systems; computers and information management; research and development; economics, costs and markets; logistics and operations management; reliability and quality control; and legality, legislation, and policy

Spatial and Temporal Study of Heat Transport of Hydrothermal Features in Norris Geyser Basin, Yellowstone National Park

Monitoring the dynamic thermal activity in Yellowstone National Park is required by the United States Congress. The continuous monitoring is important to maintain the safety of the visitors and park service personnel, plan and relocate infrastructure, and study potential impact from nearby geothermal development including oil and gas industry. This dissertation is part of a study initiated in the early 2000s to monitor the thermal activity of dynamic areas within the Park, using airborne remote sensing imagery. This study was focused in Norris Geyser Basin, the hottest geyser basin in the park, located near the northwestern rim of the Yellowstone’s caldera. The study is considered the first long-term comprehensive airborne remote sensing study in the basin which took place between August 2008 and October 2013. In this study, at least one 1-meter resolution thermal infrared image and three-band images (multispectral) were acquired and used to estimate year-to-year changes in radiant temperature, radiant flux, and radiant power from the thermal source in Norris. Presence of residual radiant flux in the ground from absorbed solar radiation and atmospheric longwave radiation was the main challenge to compere year-to-year changes in the thermal activity. This residual flux is included in the total radiant flux calculated through the remote sensing images which gives false estimates of the flux generated from the underling thermal source. Two methods were suggested in Chapters 2 and 4 of this dissertation to estimate the residual radiant flux. A method was developed in Chapter 2 to estimate the residual radiant flux in a bare ground area covered with hydrothermal siliceous sinter deposit. The method compared ground-based measurements with high spatial resolution airborne remote sensing measurements to estimate the residual radiant flux. In Chapter 4, a method was developed to estimate the residual radiant flux in the six surface classes in Norris, including bare ground, bare ground with siliceous sinter deposit, lakes and pools, river, forest, and grass. The assumptions and implications of each method were discussed to suggest a reliable method to estimate the geothermal radiant flux after subtracting the absorbed residual radiant flux. Chapter 3 provides an analysis of the four components of heat flux in the ground surface, including conduction of sensible heat, convection of sensible heat by liquid water and water vapor, and convection of latent heat by water vapor. The main purpose from the analysis was to assess the hypothesis that the convection and latent heat flux are negligible which therefore supported the results obtained from the analysis in Chapters 2 and 4