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IEEE General Meeting Power& Energy Society

作者： Lindemberg Roberto de Lima Pedro A. de Alcântara Luana C. S. Soares Camila M. V. Barros Luciano S. Barros Mathematics and Computational Modeling Graduate Program João Pessoa Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

In this paper, considering photovoltaic/battery sourced microgrids with grid-forming droop control in the load interface converter for power management, two alternative converter topologies for the battery are tested: dual boost interleaved converter and three-phase interleaved converter. These topologies are based on the conventional bidirectional buckboost converter and their employment is to allow a reduction in component sizing, improvement in power quality, and higher output voltage. Analysis of the operation of these converters in different operation modes of the microgrid is carried out, including battery normal charging, battery normal discharging, battery charging limit, and maximum state-of-charge, through simulations in MATLAB/Simulink environment. Results suggest advantages such as reduced stress on switches and battery charging current ripple for the two interleaved converters tested and the potential implementation of higher output voltage for the dual boost converter.

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Enhancing the comprehensibility of multidimensional phenomena: The optimal-entropy method for constructing composite indicators. A new index to measure the quality of the public health system in the municipalities of Minas Gerais

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Science Talks 2025年 13卷

作者： Coimbra, André Gomes Libório, Matheus P. Brunsdon, Chris Carvalho, Paulo F. Ekel, Petr Postgraduate Program in Computational Modeling and Systems State University of Montes Claros – UNIMONTES Montes Claros Brazil State University of Montes Claros – UNIMONTES Brazil Maynooth University Ireland Pontifical Catholic University of Minas Gerais Brazil

The analysis of multidimensional phenomena, such as the public health system, poses significant challenges due to the need to integrate and interpret data from different sources and natures, and composite indicators offer an aggregated and simplified view of the multidimensional phenomenon. This article proposes a method for constructing composite indicators using the optimal entropy technique, applied to the evaluation of the quality of the public health system in municipalities in Minas Gerais with more than 30 thousand inhabitants. The focus is on weighting, aiming to maximize the discrimination between the indicators, solving important problems presented by the traditional entropy weighting method. The approach uses the data entropy test and sequential quadratic programming for optimization. The results indicate that the use of linear entropy instead of Shannon entropy in the entropy test increases the variability and discriminant power of the indicators, contributing to a more accurate analysis of the quality of the public health system. © 2024 The Authors

关键词： Composite indicator Health system quality Linear entropy Operational research Optimal entropy

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Comparative Analysis between Three-Leg Interleaved and Conventional Bidirectional Converters Applied in Battery of Wind Power-Battery Unit Supplying an Islanded Microgrid

Comparative Analysis between Three-Leg Interleaved and Conve...

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IEEE General Meeting Power& Energy Society

作者： André C. Do Nascimento Giusep M. S. Ribeiro Lindemberg R. de Lima Pedro A. de Alcântara Luana C. S. Soares Luciano S. Barros Camila M. V. Barros Electrical Engineering Graduate Program Federal Institute of Pará Belém Brazil Electrical Engineering Graduate Program Federal University of Paraíba João Pessoa Brazil Mathematics and Computational Modeling Graduate Program Federal University of Paraíba João Pessoa Brazil Computer Systems Department Federal University of Paraíba João Pessoa Brazil

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Battery energy storage systems (BESS) are crucial for microgrids, enhancing dynamic performance and mitigating uncertainties inherent to isolated environments characterized by intermittent power generation. When microgrid is supplied by wind power in connection with BESS, the bidirectional DC-DC converter associated with BESS assumes a critical responsibility—ensuring the control of a wind power supplied DC-link. This permits the Voltage Source Inverter (VSI) to synthesize consistent voltages for the connected AC load. However, challenges emerge, particularly in arduous stress on switches of the bidirectional converter attributed to elevated current ripple. This stress does not only prejudice the overall system’s functioning but can also potentially leading it to shutdown in case of switch failure. Traditionally, the conventional buck-boost converter topology has been the go-to choice for this application, nevertheless, exploring novel converter topologies, such as interleaved bidirectional DC-DC converter (IBDC) has garnered attention, promising enhanced performance. This paper’s goal is to conduct a comparative analysis of the operation of these converters in an islanded microgrid supplied by a single Permanent Magnet Synchronous Generators (PMSG) small-scale wind turbine with a BESS system. Load variation with Maximum Power Point Tracking (MPPT) operation of the PMSG is performed during MATLAB/Simulink environment simulations. The results suggest significant advantages for the interleaved three-phase converter, such as reduced battery charging current ripple, switch disturbance, and voltage ripple in the DC-link.

关键词： Maximum power point trackers Magnetic separation Microgrids DC-DC power converters Wind power generation Permanent magnets Batteries Topology Wind turbines Stress

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School dropout in the Federal Network Education of Brazil: is it an inherent individual attribute or it lies on setting conditions?

School dropout in the Federal Network Education of Brazil: i...

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International Symposium on Computers in Education (SIIE)

作者： Jabson Cavalcante Dias Tadeu Lucena Da Silva Marco Antônio Juliatto Adriano Nascimento Da Paixão David Nadler Prata Federal University of Tocantins Palmas TO Brazil Statistic Department Federal University of Paraíba Paraíba PB Brazil Institute of Regional Development Graduate Program of Computational Modeling Federal University of Tocantins Palmas TO Brazil

Studies shown that school dropout among young people in Brazil corresponds to 3% of the annual GDP and is equivalent to all state and municipal spending on basic education per year. The expectation is that, each year, the country has approximately 575,000 young people without basic education due to school dropout. Many aspects can lead to greater or lesser school dropouts. An important issue that can help to understand this school phenomenon is to disentangle which attributes are inherent to the individual and which are attributed to the setting, so that public policies could be developed in favor of reducing schools’ dropouts. In this work, logistic regression methods were applied to the 2019 Nilo Peçanha Platform databases (approximately 250 thousand records). Results shown that demographic, geographic, economic, and school setting variables can be measured to understand their impacts in projecting schools’ dropouts, enabling the elaboration and carrying out of preventive and corrective actions in schools.

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An approximate MIP-DoM calculation for multi-objective optimization using affinity propagation clustering algorithm 21

An approximate MIP-DoM calculation for multi-objective optim...

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2021 Genetic and Evolutionary Computation Conference, GECCO 2021

作者： Lopes, Claudio L. V. Martins, Flávio V. C. Wanner, Elizabeth F. Deb, Kalyanmoy Postgraduate Program in Mathematical and Computational Modeling CEFET-MG BH Mg Brazil Computer Department CEFET-MG BH Mg 30510-000 Brazil Computer Department CEFET-MG BH Mg Brazil

ISBN: (纸本)9781450383516

Dominance move (DoM) is a quality indicator that compares two solution sets in a Pareto-optimal sense. The main issue related to DoM is its computational expense. A recent paper proposed a mixed-integer programming (MIP) approach for computing DoM that exhibited a computational complexity that is linear to the number of objectives and polynomial to the number of solutions. Even with this property, considering practical situations, the MIP-DoM calculation on some problems may take many hours. This paper presents an approximation method to deal with the problem using a cluster-based and divide-and-conquer strategy. Some experiments are tested, showing that the cluster based-algorithm is computationally much faster and makes a small percentage error from the original DoM value. © 2021 Owner/Author.

关键词： Multiobjective optimization

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Towards a deep learning potential for anhydrous proton transport

Towards a deep learning potential for anhydrous proton trans...

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2020 AIChE Annual Meeting

作者： Achar, Siddarth Johnson, J. Karl Computational Modeling and Simulation Program University of Pittsburgh United States Department of Chemical Engineering University of Pittsburgh United States

ISBN: (纸本)9780816911141

Standard density functional theory (DFT) molecular dynamic (MD) simulations are prohibitively expensive in terms ofcompute time and memory requirements when the size of the physical system is larger than several hundred *** alternate approach is to develop a deep learning neural network potential that can accurately reproduce DFTenergies within an error of several 10s of meVs. Neural network potentials thus allow large-scale MD simulations formuch longer times than can be achieved with DFT-MD, but with comparable accuracy. We have used the recentlypublished DeePMD [1] formalism to develop potentials for systems relevant to modeling proton transport onfunctionalized graphane. We have used DFT-MD calculations for small structures of graphane and graphanol astraining data to build reliable machine-learned potentials. Apart from energies and forces, vibrational and thermalproperties such as phonon dispersion relations and thermal expansion coefficients from DFT-MD were used to validatethe accuracy of our trained DeePMD potentials. Our goal is to develop a machine learning potential that is able toaccurately predict proton conduction on graphanol in the complete absence of water. Previous DFT calculations haveshown that graphanol a promising candidate for anhydrous proton exchange membrane applications [2]. We seek toidentify large-scale and long-time proton hopping behavior for this system through the use of machine learningpotentials. © 2020 American Institute of Chemical Engineers. All rights reserved.

关键词： Density functional theory

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Migration Guided by a Performance Index in Heterogeneous Island Models 9th

Migration Guided by a Performance Index in Heterogeneous Isl...

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9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020

作者： Pontes, Rodrigo C. A. F. Duarte, Grasiele R. Goliatt, Leonardo Computational Modeling Program Federal University of Juiz de Fora Juiz de Fora Brazil Civil Engineering Program COPPE/Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9783030637095

Evolutionary Algorithms are efficient alternatives to solve complex optimization problems. The high computational cost of these algorithms commonly motivates their implementation to run in parallel computational environments. Island Model enables the parallel implementation of Evolutionary Algorithms relatively easily to incorporate the migration operation into the evolutionary process. The inclusion of new solutions in a population, previously evolved in another population, can contribute positively to the problem’s solution quality. In this work, a performance index was added to the Island Model, aiming to indicate how efficiently each island’s population is in solving the problem according to its algorithm. Islands with higher performance indexes receive more individuals in migrations. In this way, these algorithms become more active in the evolutionary process. The experiments demonstrated that the new model solutions were as good as the solutions from each problem’s best algorithm. We also noticed that even if we remove the most efficient algorithm from the model, it still adapts and provides efficient solutions. © 2020, Springer Nature Switzerland AG.

关键词： Evolutionary algorithms

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The Fisher Geometry and Geodesics of the Multivariate Normals, without Differential Geometry

arXiv

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arXiv 2023年

作者： Lawson, Brodie A.J. Burrage, Kevin Mengersen, Kerrie Santos, Rodrigo Weber dos Centre for Data Science Queensland University of Technology Australia School of Mathematical Sciences Queensland University of Technology Australia ARC Centre of Excellence for Plant Success in Nature and Agriculture Australia Department of Computer Science University of Oxford United Kingdom Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil

Choosing the Fisher information as the metric tensor for a Riemannian manifold provides a powerful yet fundamental way to understand statistical distribution families. Distances along this manifold become a compelling measure of statistical distance, and paths of shorter distance improve sampling techniques that leverage a sequence of distributions in their operation. Unfortunately, even for a distribution as generally tractable as the multivariate normal distribution, this information geometry proves unwieldy enough that closed-form solutions for shortest-distance paths or their lengths remain unavailable outside of limited special cases. In this review we present for general statisticians the most practical aspects of the Fisher geometry for this fundamental distribution family. Rather than a differential geometric treatment, we use an intuitive understanding of the covariance-induced curvature of this manifold to unify the special cases with known closed-form solution and review approximate solutions for the general case. We also use the multivariate normal information geometry to better understand the paths or distances commonly used in statistics (annealing, Wasserstein). Given the unavailability of a general solution, we also discuss the methods used for numerically obtaining geodesics in the space of multivariate normals, identifying remaining challenges and suggesting methodological *** Codes 62B11 (primary), 62-02, 62-08 (secondary) © 2023, CC BY.

关键词： Geometry

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Urban Transportation Planning, Sustainable Cities, Big Data, Intelligent Transport Systems

SSRN

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SSRN 2024年

作者： Vieira, Túlio Moita, Gray Ribeiro, Renato Meireles, Magali Postgraduate Program in Mathematical and Computational Modeling CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Transport Engineering Department CEFET-MG Av. Amazonas 7675 Minas Gerais Belo Horizonte30510-000 Brazil Institute of Mathematical Sciences and Informatics PUC-Minas Walter Ianni 255 Minas Gerais Belo Horizonte31980-110 Brazil

This paper presents a comprehensive study on the critical role of efficient transportation systems in urban settings, particularly in developing countries. It underscores the socio-economic and environmental impacts of inadequate urban planning and highlights the disproportionate effects on the most vulnerable populations. The study proposes a novel, low-cost framework that leverages advancements in artificial intelligence, information systems, and hardware to improve the efficiency and accessibility of public transportation systems. The framework enables the construction of automatic origin-destination matrices and continuous monitoring of key transportation indicators, facilitating more accurate and timely decision-making. The paper also presents a comparative analysis of the proposed methodology with existing ones in the literature, validating its effectiveness with field-collected data. The study concludes by emphasizing thepotential of technology in addressing urban transportation challenges, contributing to the broader goal of creating sustainable and inclusive cities. This work serves as a valuable tool for urban planners in resource-constrained settings, bridging the gap between advanced technology and urban planning. © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Extreme Learning Machine with Evolutionary Parameter Tuning Applied to Forecast the Daily Natural Flow at Cahora Bassa Dam, Mozambique 9th

Extreme Learning Machine with Evolutionary Parameter Tuning ...

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9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020

作者： Martinho, Alfeu D. Ribeiro, Celso B. M. Gorodetskaya, Yulia Fonseca, Tales L. Goliatt, Leonardo Púnguè-Tete University Chimoio Mozambique Computational Modeling Program Federal University of Juiz de Fora Juiz de Fora Brazil Civil Engineering Program Federal University of Juiz de Fora Juiz de Fora Brazil

ISBN: (纸本)9783030637095

This paper proposes a hybrid approach combining an Extreme Learning Machine and a Genetic Algorithm to predict the short-term streamflow at the Cahora Bassa dam, the largest hydroelectric power plant in southern Africa. To predict the streamflows seven days ahead, the model uses as input the past river flows, information from humidity, rainfall, and evaporation measures from the lake upstream of the dam. The choice of the Extreme Learning Machine’s internal parameters, crucial for excellent model performance, is performed by a Genetic Algorithm. A set of five metrics was used to assess the performance of the hybrid approach. The computational experiments show the proposed approach outperforms other machine learning methods such as ElasticNet linear model, Support Vector Machines, and Gradient Boosting. However, the ELM prediction model overestimates higher flows. The approach arises as a practical tool to predict the streams which have the potential to help the dam operations balancing the needs of energy production and the safety of the population living downstream of the dam. © 2020, Springer Nature Switzerland AG.

关键词： Genetic algorithms

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