Fault location in underground systems using artificial neural networks and PSCAD/EMTDC

The need for high reliability and environmental concerns are making the underground networks the most appropriate choice of energy distribution. However, like any other system, underground distribution systems are not free of failures. In this context, this work presents an approach to study underground systems using computational tools by integrating the software PSCAD/EMTDC with artificial neural networks to assist fault location in power distribution systems. Targeted benefits include greater accuracy and reduced repair time. The results presented here shows the feasibility of the proposed approach. © 2012 IEEE.

Florescimento e frutificação do maracujazeiro-amarelo submetido à iluminação artificial, irrigação e sombreamento

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

Caracterização físico-química de frutos de maracujazeiro-amarelo submetidos à iluminação artificial, irrigação e sombreamento

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Crescimento, partição de fotoassimilados e produção de rizomas em taro cultivado sob sombreamento artificial

O cultivo associado é uma alternativa para culturas de ciclo longo e propriedades com limitação de área. Todavia, para o estabelecimento de associações, há necessidade de conhecer a tolerância das espécies ao sombreamento. O trabalho objetivou avaliar o crescimento de parte aérea, partição de fotoassimilados e produção de rizomas em plantas de taro 'Japonês' cultivadas sob intensidades e períodos de sombreamento artificial. Utilizou-se o delineamento de blocos ao acaso, com 13 tratamentos e quatro repetições. Os tratamentos foram constituídos de quatro intensidades de sombreamento (controle = pleno sol; 18; 30 e 50% de sombra, mantidos durante o ciclo todo), além da implementação das intensidades de sombra de 18; 30 e 50%, em três períodos (inicial = 0 a 3; intermediário = 3 a 6 e final = 6 a 9 meses). Aos 60; 90; 120; 150; 180; 210; 240 e 270 dias após o plantio (dat), avaliou-se crescimento de planta e partição de massa entre órgãos e aos 270 dat a produção de rizomas. Plantas sob sombreamento, sobretudo nas maiores intensidades e durante o ciclo todo, apresentaram maior produção de biomassa de parte aérea e de rizomas-mãe e filhos pequenos, em detrimento de rizomas-filho grandes, médios e comerciáveis. A intensidade de 18% de sombra, durante todo o ciclo ou nos períodos inicial e intermediário, foi a que menos afetou o desenvolvimento das plantas e produção de biomassa de rizomas-filho comerciáveis.

Effectiveness of Photogem (R) activated by LED on the decontamination of artificial carious bovine dentin

The aim of this study was the evaluation of the effectiveness of photodynamic therapy on the decontamination of artificially induced carious bovine dentin, using Photoge(R) as the photosensitizer agent and an LED device as a light source. Dentin samples obtained from bovine incisors were immersed in sterile broth supplemented by Lactobacillus acidophillus 10(8) colony formation units (CFU) and Streptococcus mutans 10 8 CFU. Different concentrations of photosensitizer, PA = 1 mg/ml, PB = 2 mg/ml, and PC = 3 mg/ml, and two fluences, D = 24 J/cm(2) and D = 48 J/cm(2), were investigated. After CFU counting per milligram of carious dentin and statistical analysis, we observed that the photodynamic therapy (PDT) parameters used were effective for bacterial reduction in the in vitro model under study. The best result was achieved with the application of Photoge(R) at 2 mg/ml and photoactivated under 24 J/cm(2) showing a survival factor of 0.14. At higher photosensitizer concentrations, a higher dark toxicity was observed. We propose a simple mathematical expression for the determination of PDT parameters of photosensitizer concentration and light fluence for different survival factor values. Since LED devices are simpler and cheaper compared to laser systems, it would be interesting to verify their efficacy as a light source in photodynamic therapy for the decontamination of carious dentin.

EFFICIENT FAULT LOCATION IN UNDERGROUND DISTRIBUTION SYSTEMS THROUGH OPTIMUM-PATH FOREST

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

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Application of intelligent systems for specification of automotive equipments using LEDs

The advantages offered by the electronic component light emitting diode ( LED) have caused a quick and wide application of this device in replacement of incandescent lights. However, in its combined application, the relationship between the design variables and the desired effect or result is very complex and it becomes difficult to model by conventional techniques. This work consists of the development of a technique, through artificial neural networks, to make possible to obtain the luminous intensity values of brake lights using LEDs from design data. (C) 2005 Elsevier B.V. All rights reserved.

Application of neural networks to identify features of dynamical grounding systems

The accurate identification of features of dynamical grounding systems are extremely important to define the operational safety and proper functioning of electric power systems. Several experimental tests and theoretical investigations have been carried out to obtain characteristics and parameters associated with the technique of grounding. The grounding system involves a lot of non-linear parameters. This paper describes a novel approach for mapping characteristics of dynamical grounding systems using artificial neural networks. The network acts as identifier of structural features of the grounding processes. So that output parameters can be estimated and generalized from an input parameter set. The results obtained by the network are compared with other approaches also used to model grounding systems.

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Implementation of two-stage Hopfield model and its application in nonlinear systems

This paper presents an efficient neural network for solving constrained nonlinear optimization problems. More specifically, a two-stage neural network architecture is developed and its internal parameters are computed using the valid-subspace technique. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty or weighting parameters for its initialization.

A novel approach to robust parameter estimation using neurofuzzy systems

A novel approach for solving robust parameter estimation problems is presented for processes with unknown-but-bounded errors and uncertainties. An artificial neural network is developed to calculate a membership set for model parameters. Techniques of fuzzy logic control lead the network to its equilibrium points. Simulated examples are presented as an illustration of the proposed technique. The result represent a significant improvement over previously proposed methods. (C) 1999 IMACS/Elsevier B.V. B.V. All rights reserved.

Analysis of forecasting capabilities of ground surfaces valuation using artificial neural networks

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

Sub-Optimal Orbital Maneuvers for Artificial Satellites

The determination of a specific orbit and the procedure to calculate orbital maneuvers of artificial satellites are problems of extreme importance in the study of orbital mechanics. Therefore, the transferring problem of a spaceship from one orbit to another, and the attention due to this subject has in increased during the last years. Many applications can be found in several space activities, for example, to put a satellite in a geostationary orbit, to change the position of a spaceship, to maintain a specific satellite's orbit, in the design of an interplanetary mission, and others. The Brazilian Satellite SCD-1 (Data Collecting Satellite) will be used as example in this paper. It is the first satellite developed entirely in Brazil, and it remains in operation to this date. SCD-1 was designed, developed, built, and tested by Brazilian scientists, engineers, and technicians working at INPE (National Institute for Space Research, and in Brazilian Industries. During the lifetime, it might be necessary do some complementary maneuvers, being this one either an orbital transferring, or just to make periodical corrections. The purpose of transferring problem is to change the position, velocity and the satellite's mass to a new pre determined state. This transfer can be totally linked (in the case of "Rendezvous") or partially free (free time, free final velocity, etc). In the global case, the direction, the orientation and the magnitude of the thrust to be applied must be chosen, respecting the equipment's limit. In order to make this transferring, either sub-optimal or optimal maneuvers may be used. In the present study, only the sub-optimal will be shown. Hence, this method will simplify the direction of thrust application, to allow a fast calculation that may be used in real time, with a very fast processing. The thrust application direction to be applied will be assumed small and constant, and the purpose of this paper is to find the time interval that the thrust is applied. This paper is basically divided into three parts: during the first one the sub-optimal maneuver is explained and detailed, the second presents the Satellite SCD-1, and finally the last part shows the results using the sub-optimal maneuver applied to the Brazilian Satellite.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.