Artigos em Revistas

Renewable energy sources based on photovoltaic generation (PVG) are promising energy alternatives to complement conventional, as the diesel thermal plants in cities remote in the Amazon Region. The allocation and sizing of generators for DG application is a challenging problem, with technical and economic implications. The PVG presents added difficulty, as it is a function of environmental conditions, mainly temperature and solar radiation. This paper proposes an analytical methodology to allocate and size active power PVG units with embedded DC/AC inverter to be integrated as concentrated or dispersed generation in isolated medium voltage electrical grids. The proposed methodology considers multiple objectives: improving the electrical grid voltage profile; reducing active power losses and the diesel generation participation, reduction in diesel and in the pollution. The global obtained solution of the proposed method is a weighted commitment to these goals, presenting different weights according to priorities established in the electrical system planning.

C. GONÇALVES, J. P. A. VIEIRA, M. E. L. TOSTES, B. C. BERNARDES

The Brazilian electrical utility companies must meet continuity indicators for energy supply, which are represented by the indices of average interruption duration and frequency, according to targets established by the Brazilian Regulatory Agency for Electrical Energy (ANEEL). In a nationwide base, ANEEL has defined 30 clusters, each one having specific targets for Customer Average Duration Interruption Index and Customer Average Frequency Interruption Index; still, very frequently the utility distribution companies are financially penalised for not meeting these indicator targets. This study proposes a decision support system based on machine learning techniques so that the utility distribution companies can emulate the characteristics and procedures used by the ANEEL, and help in obtaining more adequate customer groups to evaluate the duration and frequency indicators. The proposed system was applied in a case study of a distribution utility whose supply area is located in the Brazilian Amazonia. The methodology proved to be adequate for seeking better customer grouping configurations that could result in a decrease in goal violations as well as providing more consistent goals, considering the specific characteristics of each distribution utility. Although this methodology was applied to a Brazilian scenario it also can be applied to other distribution utilities worldwide.

GUILHERME AUGUSTO BARROS CONDE, FÁBIO CORREA DOS SANTOS, ADAMO LIMA DE SANTANA, ROGÉRIO DIOGNE SILVA, CARLOS RENATO LISBOA FRANCÊS, MARIA EMÍLIA DE LIMA TOSTES

This paper presents a new methodology for identifying internal faults and inrush currents in power transformers based on the gradient of the differential current. The technique is to calculate the angle of the gradient vector along the curve of the differential current in the data window and through waveforms recognition techniques to identify the type of occurrence. The gradient vector of a function corresponds to a vector tangent to the curve at the point under consideration. The gradient vector, to move along the curve of the differential current, changes its angle to the horizontal reference. The behavior of the gradient vector angle, through statistical calculations will be used to identify the occurrence of internal faults or the presence of inrush currents. The method was tested by simulating various types of internal and external faults and also several cases of inrush currents in a power transformer which is modeled by the EMTP/ATP software and also by implementing the algorithm in MATLAB , presenting highly satisfactory results.

RAIDSON JENNER N. DE ALENCAR, UBIRATAN HOLANDA BEZERRA

Este artigo apresenta uma proposta de análise da qualidade de energia elétrica através do indicador THD (total de distorção harmônica) e do Fator de Potência em sistema trifásico de baixa tensão utilizando um sistema de inferência Fuzzy, tendo como resultado uma nova proposta para avaliação da qualidade de energia.

BENEVALDO PEREIRA GONÇALVES, JORGE ALMEIDA BRITO JUNIOR, JANDECY CABRAL LEITE, CARLOS TAVARES DA COSTA JUNIOR, MARIA EMÍLIA DE LIMA TOSTES

The unbalanced electric networks analysis has upgraded from the sequence components frame models to the phase-coordinates frame models. The electric power distribution systems for industrial plants, commercial buildings or residential costumers can contain several network unbalances that can be easily represented by phase-coordinates models. The phase-coordinates models can be used for the analysis of distribution systems at fundamental frequency as well as for other frequencies and are well suited for the harmonic analysis of unbalanced networks. The formulation of the frequency scan method in a phase-coordinates frame, besides allowing for a more accurate modeling of unbalanced networks, allows the specification of harmonic injections of any type: single-phase or three-phase of the desired sequence. Therefore, it is possible to study the effect of the non-linear loads according to its features and connection to the circuit. This paper develops the phase-coordinates model of an unbalanced distribution power system, considering the representation of three-phase transformer banks of three or two single-phase transformers, as well as other asymmetries that can be found in the electrical systems of industrial plants, commercial or service facilities. The present model improves the representation of the single-phase transformer with mid-tap on the secondary side. The frequency scan based on the present model is tested with a simple example of a four-wire delta system that highlights the possibilities of the model in phase-coordinates.

JANDECY CABRAL LEITE, IGNACIO PEREZ ABRIL, MARIA EMÍLIA DE LIMA TOSTES, ROBERTO CÉLIO LIMÃO DE OLIVEIRA

This paper presents a method to determine the individual harmonic influence of multiple harmonic-producing loads in an electrical power system. This method is based in the development of statistical models from measurement data using the polynomial-regression technique. A real case study is presented in order to show the performance of the proposed method.

MARIA EMÍLIA DE LIMA TOSTES

This paper proposes a new approach to fault diagnosis in electrical power systems, which presents an aspect little explored in the literature that is the protective device failure detection together with the fault section estimation, since the majority of the methodologies so far proposed to fault diagnosis are limited to the fault section estimation alone. The proposed methodology makes use of operation states of protective devices as well as information related to the protection philosophy. Initially, these data undergo a preprocessing step to convert the format of 0 and 1 to percentage values. The conversion to percentage values allows the use of artificial neural networks, whose numbers of inputs do not depend on the number of alarms of the protection philosophy, or the type of bus arrangement or the number of circuit breakers. This allows the same set of neural networks to be trained and applied in different power systems with different protection schemes and bus arrangements. The proposed system has five neural networks, each containing few neurons and requiring 30 μ s to perform fault diagnosis. The proposed system was trained considering the IEEE 57-bus system, containing different selective protection schemes, and subsequently tested in the IEEE 14-bus, 30-bus, and 118-bus systems, and Eletronorte 230-kV real power system.

WELLINGTON ALEX DOS SANTOS FONSECA, UBIRATAN HOLANDA BEZERRA, MARCUS VINÍCIUS ALVES NUNES, FABIOLA GRAZIELA NORONHA BARROS, JOAQUIM AMÉRICO PINTO MOUTINHO

One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variables of the power system itself, but also related to social–economic and climatic factors. Hence, it is imperative for the power suppliers to project and correlate these parameters. This paper presents a study of power load forecast for power suppliers, considering the applicability of wavelets, time series analysis methods and artificial neural networks, for both mid and long term forecasts. Both the periods of forecast are of major importance for power suppliers to define the future power consumption of a given region. The paper also studies the establishment of correlations among the variables using Bayesian networks. The results obtained are much more effective when compared to those projected by the power suppliers based on specialist information. The research discussed here is implemented on a decision support system, contributing to the decision making for acquisition/sale of energy at a future demand; also providing them with new ways for inference and analyses with the correlation model presented here.

ÁDAMO L. SANTANA, GUILHERME B. CONDE, LIVIANE P. REGO, CLÁUDIO A. ROCHA, DIEGO L. CARDOSO, JOÃO C.W. COSTA, UBIRATAN H. BEZERRA, CARLOS R.L. FRANCÊS

This study presents some data-mining experiments applied to electric power systems with the aim of extracting knowledge from historical data produced by the supervision, control and data acquisition system of a hydroelectric plant in Brazil. In the first experiment, statistical analysis is performed on discrete events such as Boolean events, alarms, commands, set-points and analogue quantities as electrical frequency, to display relevant aspects of the electrical system operation. Next, the results of an experiment performed on discrete events from associations describing relationships patterns among items in a database are presented. In the third experiment, a decision tree is used to reveal relationships among several analogue variables as: the relationship between the downstream water level and generated power, for example. In the fourth experiment, a decision tree is designed to detect if the hydro generator operation is violating any constraint imposed by its capability curve, also indicating which limit is extrapolated. These experiences contribute to successfully show the data-mining applicability to power systems, to improve the management of hydroelectric power plants operation, maintenance and planning, besides also contributing to establish a culture of its usage in the electrical industry.

I. OHANA, U.H. BEZERRA, J.P.A. VIEIRA

in this letter, a methodology is proposed for automatically (and locally) obtaining the shape factor c for the gaussian basis functions, for each support domain, in order to increase numerical precision and mainly to avoid matrix inversion impossibilities. the concept of calibration function is introduced, which is used for obtaining c. the methodology developed was applied for a 2-d numerical experiment, which results are compared to analytical solution. this comparison revels that the results associated to the developed methodology are very close to the analytical solution for the entire bandwidth of the excitation pulse. the proposed methodology is called in this work local shape factor calibration method (lsfcm).

PÉRICLES L. MACHADO, RODRIGO M.S. DE OLIVEIRA, WASHINGTON C.B. SOUZA, RAMON C.F. ARAÚJO, MARIA E.L. TOSTES, CLÁUDIO GONÇALVES