Inclusão das representações de gary e de skilling-umoto em modelos de linhas de transmissão trifásicas: Aplicação em simulações de transitórios eletromagnéticos em sistemas de energia elétrica

Pós-graduação em Engenharia Elétrica - FEIS

Projeto de controladores robustos para acionamento de um motor de indução trifásico

Pós-graduação em Engenharia Elétrica - FEIS

Contribuições para o estudo de atribuição de responsabilidades em circuitos elétricos de baixa tensão

Pós-graduação em Engenharia Elétrica - FEB

Preparation and structural characterization of vulcanized natural rubber nanocomposites containing nickel-zinc ferrite nanopowders

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Structural, electronic structure and magnetic studies of SmFe1-xNixO3 (x <= 0.5)

We present the structural, electronic structure and magnetic studies of Ni doped SmFeO3. The X-ray diffraction (XRD) studies confirm the single phase nature of the samples having orthorhombic Pbnm structure and the unit-cell volume is decreasing with the increase of Ni concentration. X-ray absorption spectroscopy (XAS) studies on O K. Fe L-3.2, Ni L-3.2 and Sm M-5.4 edges of SmFe1-xNixO3 (x <= 0.5) samples along with the reference compounds revealed the homo-valence state of Fe and Ni in these materials. From magnetization studies it has been observed the materials exhibit ferromagnetic and anti-ferromagnetic sub-lattices, which are strongly dependent on the thermo-magnetic state of the system. (C) 2010 Elsevier B.V. All rights reserved.

Loss minimization by the predictor-corrector modified barrier approach

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Predictor-Corrector Primal-Dual Interior Point Method for Solving Economic Dispatch Problems: A Postoptimization Analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Structural transition on Pb1-xSrxTiO3 produced by chemical method

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel neural model to electrical load forecasting in transformers

The paper describes a novel neural model to electrical load forecasting in transformers. The network acts as identifier of structural features to forecast process. So that output parameters can be estimated and generalized from an input parameter set. The model was trained and assessed through load data extracted from a Brazilian Electric Utility taking into account time, current, tension, active power in the three phases of the system. The results obtained in the simulations show that the developed technique can be used as an alternative tool to become more appropriate for planning of electric power systems.

Resolvendo problemas de fluxo de potência ótimo DC através de uma rede de hopfield modificada

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fault Current Limiter Using YBCO Coated Conductor-The Limiting Factor and Its Recovery Time

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Fast-forward/fast-backward substitutions on vector computers

This paper deals with approaches for sparse matrix substitutions using vector processing. Many publications have used the W-matrix method to solve the forward/backward substitutions on vector computer. Recently a different approach has been presented using dependency-based substitution algorithm (DBSA). In this paper the focus is on new algorithms able to explore the sparsity of the vectors. The efficiency is tested using linear systems from power systems with 118, 320, 725 and 1729 buses. The tests were performed on a CRAY Y MP2E/232. The speedups for a fast-forward/fast-backward using a 1729-bus system are near 19 and 14 for real and complex arithmetic operations, respectively. When forward/backward is employed the speedups are about 8 and 6 to perform the same simulations.

Allocation of protective devices in distribution circuits using nonlinear programming models and genetic algorithms

The optimized allocation of protective devices in strategic points of the circuit improves the quality of the energy supply and the system reliability index. This paper presents a nonlinear integer programming (NLIP) model with binary variables, to deal with the problem of protective device allocation in the main feeder and all branches of an overhead distribution circuit, to improve the reliability index and to provide customers with service of high quality and reliability. The constraints considered in the problem take into account technical and economical limitations, such as coordination problems of serial protective devices, available equipment, the importance of the feeder and the circuit topology. The use of genetic algorithms (GAs) is proposed to solve this problem, using a binary representation that does (1) or does not (0) show allocation of protective devices (reclosers, sectionalizers and fuses) in predefined points of the circuit. Results are presented for a real circuit (134 busses), with the possibility of protective device allocation in 29 points. Also the ability of the algorithm in finding good solutions while improving significantly the indicators of reliability is shown. (C) 2003 Elsevier B.V. All rights reserved.

Interior point algorithm for linear programming used in transmission network synthesis

This article presents a well-known interior point method (IPM) used to solve problems of linear programming that appear as sub-problems in the solution of the long-term transmission network expansion planning problem. The linear programming problem appears when the transportation model is used, and when there is the intention to solve the planning problem using a constructive heuristic algorithm (CHA), ora branch-and-bound algorithm. This paper shows the application of the IPM in a CHA. A good performance of the IPM was obtained, and then it can be used as tool inside algorithm, used to solve the planning problem. Illustrative tests are shown, using electrical systems known in the specialized literature. (C) 2005 Elsevier B.V. All rights reserved.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.