Navegando por Autor "Vasconcelos, João Antônio de"

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Distribution network reconfiguration using iterative branch exchange and clustering technique.
(2023) Pereira, Ezequiel C.; Barbosa, Carlos Henrique Nogueira de Resende; Vasconcelos, João Antônio de
The distribution network reconfiguration problem (DNRP) refers to the challenge of searching for a given power distribution network configuration with better operating conditions, such as minimized energy losses and improved voltage levels. To accomplish that, this paper revisits the branch exchange heuristics and presents a method in which it is coupled with other techniques such as evolutionary metaheuristics and cluster analysis. The methodology is applied to four benchmark networks, the 33-, 70-, 84-, and 136-bus networks, and the results are compared with those available in the literature, using the criteria of the number of power flow executions. The methodology minimized the four systems starting from the initial configuration of the network. The main contributions of this work are the use of clustering techniques to reduce the search space of the DNRP; the consideration of voltage regulation banks and voltage-dependent loads in the feeder, requiring the addition of a constraint to the mono-objective model to guarantee the transferred load will be supplied at the best voltage magnitude level, and the application of the methodology in real distribution networks to solve a set of 81 real DNRPs from CEMIG-D (the distribution branch of the Energy Company of Minas Gerais). Four out of those are presented as case studies to demonstrate the applicability of the approach, which efficiently found configurations with lower power and energy losses with few PF runs.
Flotação aniônica de rejeito de minério de manganês.
(2008) Lima, Rosa Malena Fernandes; Vasconcelos, João Antônio de; Silva, Gláucia Regina da
Normalmente, os fluxogramas brasileiros, tanto para o beneficiamento de minérios ricos, quanto para minérios complexos de manganês, consistem, basicamente, de fragmentação e classificação granulométrica, com o descarte da fração abaixo de 0,106mm. Logo, é muito importante para a indústria mineral desenvolver fluxogramas e processos de concentração dessas frações granulométricas, visando ao aumento da recuperação global de manganês, bem como à redução do impacto ambiental, gerado com o descarte desses finos. Nesse trabalho, são apresentados estudos comparativos de flotação aniônica de um rejeito de minério complexo sílico-carbonatado, constituído, principalmente, pelos minerais rodocrosita, rodonita, espessartia clinocloro, quartzo, anita e flogopita, usando os coletores oleato de sódio e sabão de óleo de soja com o depressor silicato de sódio. Através desses estudos, foram verificadas reduções dos teores de SiO2 da alimentação (28,10%) de cerca de 11 e 8% para o oleato de sódio e o sabão de óleo de soja, respectivamente em pH 11.
LONSA : a labeling-oriented non-dominated sorting algorithm for evolutionary many-objective optimization.
(2018) Alexandre, Rafael Frederico; Barbosa, Carlos Henrique Nogueira de Resende; Vasconcelos, João Antônio de
Multiobjective algorithms are powerful in tackling complex optmization problems mathematically represented by two or more conflicting objective functions and their constraints. Sorting a set of current solutions across non-dominated fronts is the key step for the searching process to finally identify which ones are the best solutions. To perform that step, a high computational effort is demanded, especially if the size of the solution set is huge or the mathematical model corresponds to a many-objective problem. In order to overcome this, a new labeling-oriented algorithm is proposed in this paper to speed up the solution-to-front assignment by avoiding usual dominance tests. Along with this algorithm, called Labeling-Oriented Non-dominated Sorting Algorithm (LONSA), the associated methodology is carefully detailed to clearly explain how the classification of the solution set is successfully achieved. This work presents a comparison between LONSA and other well-known algorithms usually found in the literature. The simulation results have shown a better performance of the proposed algorithm against nine chosen strategies in terms of computational time as well as number of comparisons.
A practical codification and its analysis for the generalized reconfiguration problem.
(2013) Barbosa, Carlos Henrique Nogueira de Resende; Alexandre, Rafael Frederico; Vasconcelos, João Antônio de
Distribution network reconfiguration problem is simply aimed at finding the best set of radial configurations among a huge number of possibilities. Each solution of this set ensures optimal operation of the system without violating any prescribed constraint. To solve such problem, it is important to count on efficient procedure to enforce radiality. In this paper, we propose a reliable approach to deal properly with topology constraint enabling algorithm convergence toward optimal or quasi-optimal solutions. A simple and practical codification of individuals used in evolutionary algorithms to solve the generalized reconfiguration problem is detailed in this paper. Mathematical formulation, algorithm, and simulation results are presented for the distribution reconfiguration problem, incorporating a new representation scheme which is immune to topologically unfeasible possibilities. The individual interpretation procedure is straight and it demands no additional data structure or graph preprocessing. Comparisons are made for five well-known distribution systems to demonstrate the efficacy of the proposed methodology. It is also demonstrated that optimal configurations are properly surveyed when single or multiple sources are dealt.
Robust feeder reconfiguration in radial distribution networks.
(2014) Barbosa, Carlos Henrique Nogueira de Resende; Mendes, Marcus Henrique Soares; Vasconcelos, João Antônio de
Distribution feeder reconfiguration has been an active field of research for many years. Some recent theoretical studies have highlighted the importance of smart reconfiguration for the operating conditions of such radial networks. In general, this problem has been tackled using a multi-objective formulation with simplified assumptions, in which the uncertainties related to network components have been neglected by both mathematical models and solution techniques. These simplifications guide searches to apparent optima that may not perform optimally under realistic conditions. To circumvent this problem, we propose a method capable of performing interval computations and consider seasonal variability in load demands to identify robust configurations, which are those that have the best performance in the worst case scenario. Our proposal, named the Interval Multi-objective Evolutionary Algorithm for Distribution Feeder Reconfiguration (IMOEA-DFR), uses interval analysis to perform configuration assessment by considering the uncertainties in the power demanded by customers. Simulations performed in three cases on a 70-busbar system demonstrated the effectiveness of the IMOEA-DFR, which obtained robust configurations that are capable to keep such system working under significant load variations. Moreover, our approach achieved stable configurations that remained feasible over long periods of time not requiring additional reconfigurations. Our results reinforce the need to include load uncertainties when analyzing DFR under realistic conditions.