Estimação e compensação de não linearidades inerentes aos atuadores dos processos industriais

The oscillations presents in control loops can cause damages in petrochemical industry. Canceling, or even preventing such oscillations, would save up to large amount of dollars. Studies have identified that one of the causes of these oscillations are the nonlinearities present on industrial process actuators. This study has the objective to develop a methodology for removal of the harmful effects of nonlinearities. Will be proposed an parameter estimation method to Hammerstein model, whose nonlinearity is represented by dead-zone or backlash. The estimated parameters will be used to construct inverse models of compensation. A simulated level system was used as a test platform. The valve that controls inflow has a nonlinearity. Results and describing function analysis show an improvement on system response

Controle vetorial de velocidade de uma máquina de indução sem mancais trifásica com bobinado dividido utilizando estimação neural de fluxo

This work describes the study and the implementation of the vector speed control for a three-phase Bearingless induction machine with divided winding of 4 poles and 1,1 kW using the neural rotor flux estimation. The vector speed control operates together with the radial positioning controllers and with the winding currents controllers of the stator phases. For the radial positioning, the forces controlled by the internal machine magnetic fields are used. For the radial forces optimization , a special rotor winding with independent circuits which allows a low rotational torque influence was used. The neural flux estimation applied to the vector speed controls has the objective of compensating the parameter dependences of the conventional estimators in relation to the parameter machine s variations due to the temperature increases or due to the rotor magnetic saturation. The implemented control system allows a direct comparison between the respective responses of the speed and radial positioning controllers to the machine oriented by the neural rotor flux estimator in relation to the conventional flux estimator. All the system control is executed by a program developed in the ANSI C language. The DSP resources used by the system are: the Analog/Digital channels converters, the PWM outputs and the parallel and RS-232 serial interfaces, which are responsible, respectively, by the DSP programming and the data capture through the supervisory system

Um sistema para estimação da vazão de gás de poços produzindo por Plunger Lift para vaso separador de teste em plataformas de petróleo

This work intends to analyze the behavior of the gas flow of plunger lift wells producing to well testing separators in offshore production platforms to aim a technical procedure to estimate the gas flow during the slug production period. The motivation for this work appeared from the expectation of some wells equipped with plunger lift method by PETROBRAS in Ubarana sea field located at Rio Grande do Norte State coast where the produced fluids measurement is made in well testing separators at the platform. The oil artificial lift method called plunger lift is used when the available energy of the reservoir is not high enough to overcome all the necessary load losses to lift the oil from the bottom of the well to the surface continuously. This method consists, basically, in one free piston acting as a mechanical interface between the formation gas and the produced liquids, greatly increasing the well s lifting efficiency. A pneumatic control valve is mounted at the flow line to control the cycles. When this valve opens, the plunger starts to move from the bottom to the surface of the well lifting all the oil and gas that are above it until to reach the well test separator where the fluids are measured. The well test separator is used to measure all the volumes produced by the well during a certain period of time called production test. In most cases, the separators are designed to measure stabilized flow, in other words, reasonably constant flow by the use of level and pressure electronic controllers (PLC) and by assumption of a steady pressure inside the separator. With plunger lift wells the liquid and gas flow at the surface are cyclical and unstable what causes the appearance of slugs inside the separator, mainly in the gas phase, because introduce significant errors in the measurement system (e.g.: overrange error). The flow gas analysis proposed in this work is based on two mathematical models used together: i) a plunger lift well model proposed by Baruzzi [1] with later modifications made by Bolonhini [2] to built a plunger lift simulator; ii) a two-phase separator model (gas + liquid) based from a three-phase separator model (gas + oil + water) proposed by Nunes [3]. Based on the models above and with field data collected from the well test separator of PUB-02 platform (Ubarana sea field) it was possible to demonstrate that the output gas flow of the separator can be estimate, with a reasonable precision, from the control signal of the Pressure Control Valve (PCV). Several models of the System Identification Toolbox from MATLAB® were analyzed to evaluate which one better fit to the data collected from the field. For validation of the models, it was used the AIC criterion, as well as a variant of the cross validation criterion. The ARX model performance was the best one to fit to the data and, this way, we decided to evaluate a recursive algorithm (RARX) also with real time data. The results were quite promising that indicating the viability to estimate the output gas flow rate from a plunger lift well producing to a well test separator, with the built-in information of the control signal to the PCV

Um algoritmo para estimação de estado em alimentadores de distribuição de energia elétrica com base no método da soma de potências

Most algorithms for state estimation based on the classical model are just adequate for use in transmission networks. Few algorithms were developed specifically for distribution systems, probably because of the little amount of data available in real time. Most overhead feeders possess just current and voltage measurements at the middle voltage bus-bar at the substation. In this way, classical algorithms are of difficult implementation, even considering off-line acquired data as pseudo-measurements. However, the necessity of automating the operation of distribution networks, mainly in regard to the selectivity of protection systems, as well to implement possibilities of load transfer maneuvers, is changing the network planning policy. In this way, some equipments incorporating telemetry and command modules have been installed in order to improve operational features, and so increasing the amount of measurement data available in real-time in the System Operation Center (SOC). This encourages the development of a state estimator model, involving real-time information and pseudo-measurements of loads, that are built from typical power factors and utilization factors (demand factors) of distribution transformers. This work reports about the development of a new state estimation method, specific for radial distribution systems. The main algorithm of the method is based on the power summation load flow. The estimation is carried out piecewise, section by section of the feeder, going from the substation to the terminal nodes. For each section, a measurement model is built, resulting in a nonlinear overdetermined equations set, whose solution is achieved by the Gaussian normal equation. The estimated variables of a section are used as pseudo-measurements for the next section. In general, a measurement set for a generic section consists of pseudo-measurements of power flows and nodal voltages obtained from the previous section or measurements in real-time, if they exist -, besides pseudomeasurements of injected powers for the power summations, whose functions are the load flow equations, assuming that the network can be represented by its single-phase equivalent. The great advantage of the algorithm is its simplicity and low computational effort. Moreover, the algorithm is very efficient, in regard to the accuracy of the estimated values. Besides the power summation state estimator, this work shows how other algorithms could be adapted to provide state estimation of middle voltage substations and networks, namely Schweppes method and an algorithm based on current proportionality, that is usually adopted for network planning tasks. Both estimators were implemented not only as alternatives for the proposed method, but also looking for getting results that give support for its validation. Once in most cases no power measurement is performed at beginning of the feeder and this is required for implementing the power summation estimations method, a new algorithm for estimating the network variables at the middle voltage bus-bar was also developed

Controle vetorial de velocidade de um motor de indução trifásico com estimação neural de fluxo

This work describes the study and the implementation of the speed control for a three-phase induction motor of 1,1 kW and 4 poles using the neural rotor flux estimation. The vector speed control operates together with the winding currents controller of the stator phasis. The neural flux estimation applied to the vector speed controls has the objective of compensating the parameter dependences of the conventional estimators in relation to the parameter machine s variations due to the temperature increases or due to the rotor magnetic saturation. The implemented control system allows a direct comparison between the respective responses of the speed controls to the machine oriented by the neural rotor flux estimator in relation to the conventional flux estimator. All the system control is executed by a program developed in the ANSI C language. The main DSP recources used by the system are, respectively, the Analog/Digital channels converters, the PWM outputs and the parallel and RS-232 serial interfaces, which are responsible, respectively, by the DSP programming and the data capture through the supervisory system

Estimação da demanda de instalações elétricas de edificações com múltiplas unidades de consumo

This work aims to predict the total maximum demand of a transformer that will be used in power systems to attend a Multiple Unit Consumption (MUC) in design. In 1987, COSERN noted that calculation of maximum total demand for a building should be different from that which defines the scaling of the input protection extension in order to not overestimate the power of the transformer. Since then there have been many changes, both in consumption habits of the population, as in electrical appliances, so that this work will endeavor to improve the estimation of peak demand. For the survey, data were collected for identification and electrical projects in different MUCs located in Natal. In some of them, measurements were made of demand for 7 consecutive days and adjusted for an integration interval of 30 minutes. The estimation of the maximum demand was made through mathematical models that calculate the desired response from a set of information previously known of MUCs. The models tested were simple linear regressions, multiple linear regressions and artificial neural networks. The various calculated results over the study were compared, and ultimately, the best answer found was put into comparison with the previously proposed model

Estimação de Fasores para Proteção de Sistemas Elétricos Baseada em Mínimos Quadrados e Morfologia Matemática

This work proposes a new technique for phasor estimation applied in microprocessor numerical relays for distance protection of transmission lines, based on the recursive least squares method and called least squares modified random walking. The phasor estimation methods have compromised their performance, mainly due to the DC exponential decaying component present in fault currents. In order to reduce the influence of the DC component, a Morphological Filter (FM) was added to the method of least squares and previously applied to the process of phasor estimation. The presented method is implemented in MATLABr and its performance is compared to one-cycle Fourier technique and conventional phasor estimation, which was also based on least squares algorithm. The methods based on least squares technique used for comparison with the proposed method were: forgetting factor recursive, covariance resetting and random walking. The techniques performance analysis were carried out by means of signals synthetic and signals provided of simulations on the Alternative Transient Program (ATP). When compared to other phasor estimation methods, the proposed method showed satisfactory results, when it comes to the estimation speed, the steady state oscillation and the overshoot. Then, the presented method performance was analyzed by means of variations in the fault parameters (resistance, distance, angle of incidence and type of fault). Through this study, the results did not showed significant variations in method performance. Besides, the apparent impedance trajectory and estimated distance of the fault were analysed, and the presented method showed better results in comparison to one-cycle Fourier algorithm

Identificação de modelos polinomiais narx utilizando algoritmos combinados de detecção de estrutura e estimação de parâmetros com aplicações práticas

A modelagem de processos industriais tem auxiliado na produção e minimização de custos, permitindo a previsão dos comportamentos futuros do sistema, supervisão de processos e projeto de controladores. Ao observar os benefícios proporcionados pela modelagem, objetiva-se primeiramente, nesta dissertação, apresentar uma metodologia de identificação de modelos não-lineares com estrutura NARX, a partir da implementação de algoritmos combinados de detecção de estrutura e estimação de parâmetros. Inicialmente, será ressaltada a importância da identificação de sistemas na otimização de processos industriais, especificamente a escolha do modelo para representar adequadamente as dinâmicas do sistema. Em seguida, será apresentada uma breve revisão das etapas que compõem a identificação de sistemas. Na sequência, serão apresentados os métodos fundamentais para detecção de estrutura (Modificado Gram- Schmidt) e estimação de parâmetros (Método dos Mínimos Quadrados e Método dos Mínimos Quadrados Estendido) de modelos. No trabalho será também realizada, através dos algoritmos implementados, a identificação de dois processos industriais distintos representados por uma planta de nível didática, que possibilita o controle de nível e vazão, e uma planta de processamento primário de petróleo simulada, que tem como objetivo representar um tratamento primário do petróleo que ocorre em plataformas petrolíferas. A dissertação é finalizada com uma avaliação dos desempenhos dos modelos obtidos, quando comparados com o sistema. A partir desta avaliação, será possível observar se os modelos identificados são capazes de representar as características estáticas e dinâmicas dos sistemas apresentados nesta dissertação

Estimação em modelos de tempo de falha acelerado para dados de sobrevivência correlacionados

We presented in this work two methods of estimation for accelerated failure time models with random e_ects to process grouped survival data. The _rst method, which is implemented in software SAS, by NLMIXED procedure, uses an adapted Gauss-Hermite quadrature to determine marginalized likelihood. The second method, implemented in the free software R, is based on the method of penalized likelihood to estimate the parameters of the model. In the _rst case we describe the main theoretical aspects and, in the second, we briey presented the approach adopted with a simulation study to investigate the performance of the method. We realized implement the models using actual data on the time of operation of oil wells from the Potiguar Basin (RN / CE).

Estimação clássica e Bayesiana em modelos de sobrevida com fração de cura

In Survival Analysis, long duration models allow for the estimation of the healing fraction, which represents a portion of the population immune to the event of interest. Here we address classical and Bayesian estimation based on mixture models and promotion time models, using different distributions (exponential, Weibull and Pareto) to model failure time. The database used to illustrate the implementations is described in Kersey et al. (1987) and it consists of a group of leukemia patients who underwent a certain type of transplant. The specific implementations used were numeric optimization by BFGS as implemented in R (base::optim), Laplace approximation (own implementation) and Gibbs sampling as implemented in Winbugs. We describe the main features of the models used, the estimation methods and the computational aspects. We also discuss how different prior information can affect the Bayesian estimates

Estimação de parâmetros genéticos para resistência à infecção por IMNV em camarões Litopenaeus vannamei por meio de análise de sobrevivência

The main specie of marine shrimp raised at Brazil and in the world is Litopenaeus vannamei, which had arrived in Brazil in the `80s. However, the entry of infectious myonecrosis virus (IMNV), causing the infectious myonecrosis disease in marine shrimps, brought economic losses to the national shrimp farming, with up to 70% of mortality in the shrimp production. In this way, the objective was to evaluate the survival of shrimps Litopenaeus vannamei infected with IMNV using the non parametric estimator of Kaplan-Meier and a model of frailty for grouped data. It were conducted three tests of viral challenges lasting 20 days each, at different periods of the year, keeping the parameters of pH, temperature, oxygen and ammonia monitored daily. It was evaluated 60 full-sib families of L. vannamei infected by IMNV in each viral challenge. The confirmation of the infection by IMNV was performed using the technique of PCR in real time through Sybr Green dye. Using the Kaplan-Meier estimator it was possible to detect significant differences (p <0.0001) between the survival curves of families and tanks and also in the joint analysis between viral challenges. It were estimated in each challenge, genetic parameters such as genetic value of family, it`s respective rate risk (frailty), and heritability in the logarithmic scale through the frailty model for grouped data. The heritability estimates were respectively 0.59; 0.36; and 0.59 in the viral challenges 1; 2; and 3, and it was also possible to identify families that have lower and higher rates of risk for the disease. These results can be used for selecting families more resistant to the IMNV infection and to include characteristic of disease resistance in L. vannamei into the genetic improvement programs

Melhorando a estimação de pose com o RANSAC preemptivo generalizado e múltiplos geradores de hipóteses

The camera motion estimation represents one of the fundamental problems in Computer Vision and it may be solved by several methods. Preemptive RANSAC is one of them, which in spite of its robustness and speed possesses a lack of flexibility related to the requirements of applications and hardware platforms using it. In this work, we propose an improvement to the structure of Preemptive RANSAC in order to overcome such limitations and make it feasible to execute on devices with heterogeneous resources (specially low budget systems) under tighter time and accuracy constraints. We derived a function called BRUMA from Preemptive RANSAC, which is able to generalize several preemption schemes, allowing previously fixed parameters (block size and elimination factor) to be changed according the applications constraints. We also propose the Generalized Preemptive RANSAC method, which allows to determine the maximum number of hipotheses an algorithm may generate. The experiments performed show the superiority of our method in the expected scenarios. Moreover, additional experiments show that the multimethod hypotheses generation achieved more robust results related to the variability in the set of evaluated motion directions

Implementação Hardware/Software da estimação de movimento segundo o padrão H.264

Motion estimation is the main responsible for data reduction in digital video encoding. It is also the most computational damanding step. H.264 is the newest standard for video compression and was planned to double the compression ratio achievied by previous standards. It was developed by the ITU-T Video Coding Experts Group (VCEG) together with the ISO/IEC Moving Picture Experts Group (MPEG) as the product of a partnership effort known as the Joint Video Team (JVT). H.264 presents novelties that improve the motion estimation efficiency, such as the adoption of variable block-size, quarter pixel precision and multiple reference frames. This work defines an architecture for motion estimation in hardware/software, using a full search algorithm, variable block-size and mode decision. This work consider the use of reconfigurable devices, soft-processors and development tools for embedded systems such as Quartus II, SOPC Builder, Nios II and ModelSim

Arquitetura com elevada taxa de processamento e reduzida largura de banda de memória para a estimação de movimento em vídeos digitais

Nowadays several electronics devices support digital videos. Some examples of these devices are cellphones, digital cameras, video cameras and digital televisions. However, raw videos present a huge amount of data, millions of bits, for their representation as the way they were captured. To store them in its primary form it would be necessary a huge amount of disk space and a huge bandwidth to allow the transmission of these data. The video compression becomes essential to make possible information storage and transmission. Motion Estimation is a technique used in the video coder that explores the temporal redundancy present in video sequences to reduce the amount of data necessary to represent the information. This work presents a hardware architecture of a motion estimation module for high resolution videos according to H.264/AVC standard. The H.264/AVC is the most advanced video coder standard, with several new features which allow it to achieve high compression rates. The architecture presented in this work was developed to provide a high data reuse. The data reuse schema adopted reduces the bandwidth required to execute motion estimation. The motion estimation is the task responsible for the largest share of the gains obtained with the H.264/AVC standard so this module is essential for final video coder performance. This work is included in Rede H.264 project which aims to develop Brazilian technology for Brazilian System of Digital Television

Sistema não-Intrusivo para Estimação da Direção do olhar utilizando redes neurais artificiais

The fundamental senses of the human body are: vision, hearing, touch, taste and smell. These senses are the functions that provide our relationship with the environment. The vision serves as a sensory receptor responsible for obtaining information from the outside world that will be sent to the brain. The gaze reflects its attention, intention and interest. Therefore, the estimation of gaze direction, using computer tools, provides a promising alternative to improve the capacity of human-computer interaction, mainly with respect to those people who suffer from motor deficiencies. Thus, the objective of this work is to present a non-intrusive system that basically uses a personal computer and a low cost webcam, combined with the use of digital image processing techniques, Wavelets transforms and pattern recognition, such as artificial neural network models, resulting in a complete system that performs since the image acquisition (including face detection and eye tracking) to the estimation of gaze direction. The obtained results show the feasibility of the proposed system, as well as several feature advantages.