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En este se investiga el modelado de un sistema del tipo correa transportadora con la finalidad de uso en el monitoreo y en el diagnóstico de faltas. Primeramente es discutido un modelo fenomenológico del proceso, el cual es basado en la aplicación de las leyes de la mecánica y considerando los diversos tipos de fuerza de oposición al movimiento que actúan sobre la correa. Los principales parámetros del transportador fueron estimados utilizando técnicas de identificación basadas en Mínimos Cuadrados No Recursivos. Los resultados obtenidos en estudios computacionales muestran el buen desempeño de la estrategia propuesta, cuando aplicada a datos reales colectados en el sistema de transporte de minerales de una mina de cobre al sur de Pará, Brasil. This paper investigates the modeling of a belt conveyor system for use in an automated monitoring system aiming at obtain improved energetic performance and fault diagnosis capability of the system. Firstly, a phenomenological model of the process is discussed, which is based on the laws of mechanics considering various movement opposing forces. The main parameters of the belt conveyor system model were estimated by using a non-recursive least mean square identification techniques. The results of computational studies show a good efficiency of the proposed strategies when applied to a set of data collected in a real world belt conveyor system, located at copper mine in south of Pará state, Brazil.

In this paper, the application of linear parameter-varying (LPV) identification and control methods for designing and performance evaluation of an adaptive power system stabilizer (PSS) is presented. The target control objective is to improve the damping of the dominant electromechanical oscillating mode in the power system. The performance of the LPV PSS controller has been assessed by means of experimental tests carried out on 10 kVA small-scale power system. For design purposes, an LPV ARX model was estimated from experimental tests, for a wide range of operating conditions. The LPV controller was tuned via an optimization problem, in the form of a Parameterized Linear Matrix Inequality (PLMI). The solution was obtained through a sum-of-squares relaxation. Both the LPV model and the LPV-PSS have an explicit dependency on system scheduling variables, namely the active and reactive powers measurements. Therefore, the LPV-PSS was designed to ensure stability and performance for a wide range of operating conditions, an objective usually difficult to obtain by using fixed parameter controllers.

Segundo a literatura tecnica pertinente, a logica fuzzy (nebulosa) surgiu como necessidade de controlar sistemas que, no mundo real, sao complexos demais para se obter boa modelagem matematica. Alem disso, ao adentrarmos cada vez mais na era da informacao, o conhecimento humano torna-se muito importante. Neste sentido, precisa-se de uma logica de controle que imite o conhecimento humano de uma maneira sistematica e seja de facil aplicacao em sistemas reais. Pretende-se, neste artigo, fazer um projeto de controlador difuso (ou fuzzy) para garantir constância na frequencia de saida de um gerador (60Hz), acionado por uma turbina a vapor.

Este trabalhoapresenta o projeto de um controlador robusto via programacao linear em comparacao com uma tecnica de projeto convencional via posicionamento de polos. Os controladores foram aplicados a regulacao de um sistema linear invariante no tempo. Testes foramrealizados a fim de verificar o desempenho dos controladores projetados.

Neste trabalho serão apresentados os resultados da avaliação experimental de uma metodologia de controle digital preditivo auto-ajustavel aplicada ao controle de tensão de um sistema de geração de energia de escala reduzida. Um estimador recursivo baseado no conhecido método de mínimos quadrados é utilizado na etapa de identificação do controlador preditivo proposto. A etapa de cálculo da lei de controle é realizada com o algoritmo Generalized Predictive Controller (GPC). A avaliação experimental foi realizada com testes de resposta ao degrau e rastreamento aplicados em diferentes condições operacionais do sistema de potência estudado. Para fins de comparação, também serão apresentados os resultados da avaliação de um controlador auto-ajustável que utiliza o método de alocação de pólos para a síntese do sinal de controle e três controladores digitais com parâmetros fixos.

Este artigo apresenta um estudo experimental de técnicas de identificação paramétrica aplicadas à modelagem dinâmica de um servidor web Apache. Foi desenvolvido um arranjo experimental para simular variações de carga no servidor. O arranjo é composto por dois computadores PC, sendo um deles utilizado para executar o servidor Apache e o outro utilizado como um gerador de carga, solicitando requisições de serviço ao servidor Apache. Foram estimados modelos paramétricos auto-regressivos (AR) para diferentes pontos de operação e de condição de carga. Cada ponto de operação foi definido em termos dos valores médios para o parâmetro de entrada MaxClients (parâmetro utilizado para definir o número máximo de processos ativos) e a saída percentual de consumo de CPU (Central Processing Unit) do servidor Apache. Para cada ponto de operação foram coletadas 600 amostras, com um intervalo de amostragem de 5 segundos. Metade do conjunto de amostras coletadas em cada ponto de operação foi utilizada para estimação do modelo, enquanto que a outra metade foi utilizada para validação. Um estudo da ordem mais adequada do modelo mostrou que, para um ponto de operação com valor reduzido de MaxClients, um modelo AR de 7ª ordem pode ser satisfatório. Para valores mais elevados de MaxClients, os resultados mostraram que são necessários modelos de ordem mais elevada, devido às não-linearidades inerentes ao sistema.

This paper presents an object-oriented framework to perform dynamical studies in electrical power systems. The proposed methodology is also useful to the development, upgrade and maintenance of software for power system studies. The framework was designed to be used by developers of power system software. Based on software engineering concepts, user's requirements are, firstly, mapped to atomic classes which, in turn, are subsequently used to build state-space representation for each dynamic device in the power system under study, such as electrical generators, turbines and automatic controllers. The developed framework automates the whole process of building a global system model, providing a standard space-state model suitable for implementing by direct code in C++ language. By using the proposed methodology, a power system simulation tool was specially developed to study electromechanical phenomena observed at Tucurui hydroelectric power plant, a large power plant of the Brazilian Interconnected Power System. The results were validated by comparing the simulated to the actual plant response obtained from field tests carried out at the Tucurui power plant. The results have shown a very good level of performance of the developed simulator system. The simulator was able to reproduce the dominant dynamical phenomena observed at the Tucurui power plant.

This paper presents the development and field tests of a digital damping controller designed to mitigate intra-plant electromechanical oscillations via the speed governor system of fast acting units. The controller performance is assessed on an 18-MVA diesel generating unit, at Santana Power Plant (Amapa State, Amazon Region at Northern Brazil). In order to design the damping control law, a set of parametric ARX models representing the plant dynamics at several load conditions, are previously identified from data collected on field tests. The damping controller gains are calculated by using the identified ARX models parameters as inputs to a discrete-time pole-placement design method (pole-shifting) and then embedded on a DSP based microcontroller digital system, for field tests assessment. The digital damping controller modulates the diesel engine inlet valve position according to the observed oscillation on the measured electric power, using a PWM device, which is specially developed to this application. The experimental results shown the good performance of the developed controller on damping efficiently the electromechanical oscillations observed between generating units at Santana Power Plant.

The development of software for dynamic simulation of electrical power systems requires a comprehensive range of complex studies, which encompasses many areas of electrical engineering as well as software engineering. This study aims at to develop an efficient strategy applied to the development of software tools for dynamic power systems simulations studies. The proposed strategy is based on the object-oriented creational pattern. This approach has the advantage of makes easy the application development process, by performing a mapping between block diagram model representation and the corresponding specialized classes. Firstly, a conceptual mapping between block diagram and the object-oriented paradigm, based on the Factory Method, is carried out. After that, some flexible strategies are presented in order to obtain an improved efficiency for the numerical routines, based on the Builder standard. This allows for the parameterization of the selected numerical integration techniques. The proposed strategy was evaluated by using a 4-generator multi-machine power system. The simulation results shown that the proposed strategy was able to provides a good power system dynamic performance.

This paper examines some problems encountered in the expansion of isolated electrical systems (IES) in the Amazon region, more precisely, the thermoelectric systems used in that region. Supposing a certain degree of uncertainty in energy consumption, we evaluate the expansion of thermoelectric power from firewood and diesel fuel, together with variations for costs, CO2 emissions, and number of direct jobs (NDJ) generated with the use of these technologies. The analysis is accomplished by using fuzzy multi-objective mathematical programming, and interpreting each objective function both by itself and in combination with the others, through a fuzzy multi-objective parametrization. The scenarios are defined by the energy consumption percentage increase, limited to be below some admissible maximum value, while still considering variations in the installed power. The costs, CO2 emission, and the NDJ generated are analyzed and compared with the largest values obtained with the model of crisp mathematical programming, used for the original configuration. Finally, in Section 4, we present the results and respective analyses for the finished simulation.

In this paper, the experimental results obtained in the design and implementation of a self-tunning voltage controller for a micro energy system are analyzed. Two types of indirect adaptive self-tuning schemes are proposed. Both uses a recursive estimation scheme based on the well known Least Squares method. The difference between them is the controller formulation methodology: one uses the pole placement method and the other uses the generalized predictive control. Tests shows the comparison performance of the proposed adaptive controllers versus fixed digital controllers.

This paper investigates the use of self-tuning Linear Quadratic Gaussian control with Loop Transfer Recovery (LQG/LTR), applied in Thyristor Controlled Series Capacitors (TCSC) installed in interconnected power systems. The proposed robust adaptive controller can improve power system stability margins, damping properly the system's dominant electromechanical modes. Although this TCSC controller is designed mainly to identify and damp inter-area modes, it does not affect negatively the local modes damping, therefore enhancing the overall system performance. The proposed controller properties can be verified using non-linear simulations for a four-machine power system, in different operation conditions.

Basic Production Logging can be interpreted in intermittent wells using the intelligent analysis program presents in this work, called PAINTWELL, which permits to use of same string configuration than a conventional Production Logging. This program can be performed in layered reservoir, determining the flowrate and type of the fluid for each production zone. The use of Artificial Neural Network as integrated part of this analysis program, provides a powerful tool to describe how downhole logging and advances in computer processing and interpretation data could spearhead the transformation that can take place in production logging with use of this new technique. This methodology should aid the production and reservoir engineering to solve recovery problems during the well life. The results of the production log using the PAINTWELL method is very similar to measurements using test separator in production facilities for each single perforated interval. This program was applied successfully for a typical intermittent well, which produces oil with high water cut using a gas-lift completion system.

This paper describes the design of a Power System Stabilizer synthesized by a static Artificial Neural Network. The patterns used to train the neural network are sets of controller parameters, previously calculated for several system operation points using a pole-shifting method. This neural network stabilizer then operates, in a gain-scheduling scheme, in accordance with the values of active and reactive powers furnished by the generator to the power system, but with soft transition variations of the controller parameters. The trained neural network presents, as its main characteristic, almost uniform values for all the stabilizer parameters when the system synchronous machine is generating reactive power, but these same parameters suffer great variations when the machine is absorbing reactive power. Simulation tests presented show very good performance for the proposed Neural PSS, when compared with a fixed-parameter stabilizer, corroborating the main characteristic of the proposed stabilizer.

Este trabalho propõe uma estratégia fuzzy, do tipo Rede de Controladores Locais, aplicável à melhoria da estabilidade dinâmica em sistemas elétricos de potência, visando compensar possíveis perdas de sintonia devido à ocorrência de variações nas condições operacionais da planta. A adaptação dos ganhos do controlador fuzzy é efetuada on-line, interpolando-se os ganhos de um conjunto finito de controladores locais fixos. Ao ocorrer variações nas condições operacionais da planta, os ganhos da lei de controle são ajustados automaticamente de modo a manter satisfatório o desempenho do sistema de controle. O desempenho do controle foi avaliado através de estudos de simulação, utilizando-se um modelo dinâmico não-linear, do tipo máquina barra infinita. Os resultados mostram que o emprego da estratégia proposta permite obter melhorias no desempenho dinâmico do sistema.

This paper is concerned with the evaluation of a robust adaptive control technique applied to damp power systems oscillations. This robust technique is based on the LQG/LTR control method, used in a self-tuning way. The stabilizer synthesized with this method is used in a TCSC device to increase the damping of low frequency electromechanical oscillations modes. The method is evaluated in a 4-machine benchmark system and simulation results show that the proposed controller presents a good performance, improving power system stability margins.

This work presents a control strategy applied to a doubly fed induction machine (DFIM) for wind generation in a medium power system. In order to control the active power and reactive power exchange between the machine and the grid, the rotor is fed by a link of AC-DC-AC converters modeled as voltage and controlled current sources, respectively. The control of the rotor-side converter is realized by fuzzy controllers whose performances are compared with that of conventional Proportional-Integral controllers. The control of the grid-side converter is carried out by a control block based on the instantaneous power theory and it is controlled to maintain the DC link voltage constant and to improve the power factor of the system. Some design considerations of the control schemes are discussed. A multimachine model is used in digital simulations to assess the dynamic behavior of the power system control scheme proposed in this work.

This paper presents the design of a Power System Stabilizer synthesized using an Artificial Neural Network. The patterns used in the network training are sets of controller parameters, previously calculated for several system operation points using the pole-placement method. The trained network presents, as its main characteristic, uniform values for all the stabilizers parameters when the system synchronous machine is generating reactive power, but these same parameters suffer great variations when reactive power is being absorbed by the machine. Simulation tests show very good performance for the proposed Neural PSS, when compared with a fixed-parameter stabilizer.

This work presents a digital adaptive control method, where the controller is designed using a self-tuning structure. Parametric models are identified off line for several differently selected operating points of the plant to form a Local Model Network (LMN), and saved as a look-up table in a microcomputer real time control. During working, a supervisor identifies the actual condition of operation and uses a set of logic fuzzy rules to estimate a plant model as a function of the рrе-identified models for the nearest selected operating points. Then, using this plant model, the controller is designed. This method is employed in the design of a Power System Stabilizer (PSS) and the control obtained for a test with two severe faults showed good performance for this PSS, when compared to a conventional (non adaptive) one.

This work presents a digital adaptive Power System Stabilizer (PSS) which operates in a self-tuning structure. The parametric models are identified off line for several differently selected operating points in a PxQ plane (active and reactive powers) to form a Local Model Network (LMN), and saved under the form of a look-up table in a microcomputer real time control. During working, a supervisor identifies the actual condition of operation and uses a set of logic fuzzy rules to estimate a plant model as a function of the pre-identified models for the nearest selected operating points. Then, using this plant model, the PSS is designed. The tests presented show good performance for this PSS, when compared to a conventional (non adaptive) one.

This work presents a laboratory kit designed for the purpose of controlling temperature in liquids. Many different control strategies can be easily evaluated by using this low cost kit. The results of comparative tests with PID controllers designed by two pole placement techniques are presented comprising the following alternatives: a controller with fixed parameters; and an adaptive controller using on-line recursive parameter estimation. These tests show the superior performance obtained with the adaptive strategy. Others interesting experiences comparing alternative control techniques could also be realised by using this inexpensive and useful laboratory kit.

Este trabalho apresenta o desenvolvimento e resultados de testes de campo de um estabilizador de sistemas de potência (ESP) digital, destinado ao amortecimento de oscilações eletromecânicas em sistemas interligados. Os testes experimentais foram efetuados em uma das unidades hidrogeradoras, de 350 MVA, da UHE de Tucuruí. A lei de controle amortecedor do ESP digital foi embarcada em um sistema de hardware baseado em um controlador digital de sinais (DSPIC 30f5011). A estrutura da lei de controle é na forma canónica RST, de tempo discreto, sendo os parâmetros do controlador calculados através da técnica de deslocamento radial de pólos. Para fins de projeto, a dinâmica da planta, no ponto de operação considerado, foi representada por um modelo paramétrico, o qual foi estimado a partir de dados medidos em campo. Os resultados experimentais mostraram um excelente desempenho do ESP digital no amortecimento de um dos modos eletromecânicos observável na UHE de Tucuruí. This paper presents the development and results of field tests of a digital power system stabilizer (PSS), designed for the damping of electromechanical oscillations in interconnected systems. Experimental tests were performed in a 350 MVA hydrogenerator unit, of the Tucurui Power Plant. The PSS damper control law was embedded in a digital hardware system based on a digital signal controller (DSPIC 30f5011). The structure of the control law is in discrete time RST canonical form, and the controller parameters were calculated using the pole shifting technique. For purposes of the controller design, the dynamics of the plant, in the considered operating point, was represented by a parametric model, which was estimated from data measured in the field. The experimental results showed an excellent performance of the damping of electromechanical modes observable UHE Tucurui by digital PSS.

This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identification parametric techniques are used in order to obtain a representative model for the controller design. With a suitable model, controllers of proportional integral type are designed, based on the root locus and fuzzy systems strategies, in order to improve the voltage regulation of the microgenerator system.

Tucurui's hydroelectric power plant, the second largest Brazilian electric plant, is one proper site to deal with bad damping electromechanical oscillations problem, because of its interconnection with two others large electrical systems (CHESF and FURNAS). The most usual way to minimize this problem, increasing power system dynamic stability margins, is to apply power system stabilizers, which are responsible for creating an additional damping signal in the generator excitation system. Tucurui's power system stabilizers, although fixed-parameter controllers, must be tuned properly to have good performance in any system operating condition. This work objective is to obtain updated linear system models from local signals measured in Tucurui's generators. System identification techniques, recursive and nonrecursive, have been used to obtain these linear models, that will be used later in fixed-parameter or even adaptive power system stabilizers design.

This work presents a digital adaptive power system stabilizer (PSS) which operates in a gain scheduling scheme. Its parameters are designed for several differently selected operating points in a PxQ plane (active and reactive powers), and saved offline under the form of a look-up table in the microcomputer that implements the control action. The complete set of all designed PSSs is called a local compensator network (LCN). During working, the a fuzzy logic based supervisor identifies the current operating point, and synthesizes the PSS parameters from the LCN. The test presented shows good performance for this PSS, when compared to a conventional (nonadaptive) one.

This work presents a digital adaptive power system stabilizer (PSS) which operates in a self-tuning structure. The parametric models are identified off line for several differently selected operating points in a P/spl times/Q plane (active and reactive powers) to form a local model network (LMN), and saved off-line under the form of a look-up table in the microcomputer that implements the control action. During working, a fuzzy logic based supervisor identifies the current operating point and determines the plant model from the LMN. Then, using this plant model, the PSS control law is designed based on the generalized predictive control (GPC) method. The tests presented show good performance for this PSS, when compared to a conventional (nonadaptive) one.

This work presents a Local Model Network (LMN) neuro-fuzzy control strategy for stability and voltage regulation improvements in electric power systems. At each sample time, the control output is calcutated from a smooth (fuzzy) switching among a set of pre-designed linear local controllers. The local controllers are designed from ARX local models identified from field tests, for several plant operating conditions. The proposed strategy was assessed, by simulation studies, using a high order nonlinear power system dynamic model. The results show that a remarkable voltage regulation as well as dynamic stability improvements can be obtained when applying the proposed neuro-fuzzy strategy.