Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

Mit der Automatisierung geht stets erneut die Frage nach der Funktionsallokation einher. Diese lässt sich vereinfacht wie folgt formulieren: Welche Aufgaben bzw. Funktionen soll die Automation und welche der Mensch ausführen? Abwendend von traditionellen Ansätzen maximaler Automatisierung und statischer Aufgabenteilungen haben sich heute dynamische Ansätze der Funktionsallokation bzw. kooperativen Automation durchgesetzt, bei denen sich Operateur und Automation die Kontrolle für bestimmte Aufgaben während des Prozesses teilen oder übergeben können. Die Ausgestaltung dieser kooperativen Automation wirft insbesondere zwei Fragen auf: Wer hat (wann wieviel) Kontrolle? Und wer entscheidet darüber? Diese Fragen nach Kontrolle und Autorität bilden die Grundlage für das theoretische und empirische Anliegen dieser Arbeit. Auf Basis einer umfangreichen literaturbasierten Diskussion der Fragen wird ein eigenes Rahmenmodell vorgeschlagen. Dieses erlaubt, anhand der Aspekte Authorität und Kontrolle, konkrete und trennscharfe Kooperationsformen zwischen Operateur und Automation zu beschreiben. Der einfachste Fall einer Zusammenarbeit zwischen Operateur und Automation wird sodann in einer Studie mit 81 Operateuren untersucht. Es interessiert, ob sich die Leistung von Operateuren, welche bei der Prozessüberwachung und -führung optional mit einer automatischen Unterstützung zusammen arbeiten können, im Vergleich zu Operateuren ohne eine solche automatische Unterstützung verbessert. Für bestimmte Leistungsindikatoren können in der Tat höchst positive Effekte der automatischen Unterstützung ermittelt und konkrete Interaktionsmuster der Operateure in der Kooperation mit der automatischen Unterstützung beschrieben werden. Die theoretischen und empirischen Ergebnisse der diesere Arbeit liefern eine Grundlage für differenzierte Bewertungen und somit auch Empfehlungen spezifischer Kooperationsformen, die gerade im Hinblick auf die Ausgestaltung kooperativer Automation noch rar sind. / Automation has always been accompanied by the question of function allocation, that is: which tasks and/or functions should be executed by the machine vs. by the human? Today, dynamic function allocation or cooperative automation has become the dominant approach, which allows for a more flexible sharing and trading of control between operator and automation. However, how cooperative automation is put into practice remains mainly an open question. Two central aspects have to be dealt with: Who should execute a certain function, i.e. have control (and when)? And who should decide about it? These questions of control and authority are central to this thesis. Based on an extensive literature review, a theoretical model is proposed. This model allows for a classification of specific forms of control between operator and automation based on authoriy and control. In an empirical study the effect of the most simple form of cooperation is investigated regarding its effect on performance of 81 operators. It is assumed that supervisory control performance of operators benefits from this form of cooperation with automation. Thereby supervisory control performance of operators cooperating with automation is compared to performance of operators who were not provided with such automation, i.e. had to supervise and control fully manually. Results show, that cooperation with automation has a positive effect on certain performance indicators. Also interaction patterns of operators cooperating with automation are investigated in detail. Both, the theoretical and empirical approach in this thesis provide a profound basis which allows for a more sophisticated evaluation and recommendation of forms of cooperation between operator and automation. So far, such recommendations are rare in Human Factors Research but they are desperately needed, especially when it comes to the implementation of forms of cooperative automation.

A discrete event system is a system whose state space is given by a discrete set and whose state transition mechanism is event-driven i.e., its state evolution depends only on the occurrence of discrete events over the time. These systems are used in many fields of application (telecommunication networks, aeronautics, aerospace,...). The validity of these systems is then an important issue and to ensure it we can use supervisory control methods. These methods consist in imposing a given specification on a system by means of a controller which runs in parallel with the original system and which restricts its behavior. In this thesis, we develop supervisory control methods where the system can have an infinite state space and the controller has a partial observation of the system (this implies that the controller must define its control policy from an imperfect knowledge of the system). Unfortunately, this problem is generally undecidable. To overcome this negative result, we use abstract interpretation techniques which ensure the termination of our algorithms by overapproximating, however, some computations. The aim of this thesis is to provide the most complete contribution it is possible to bring to this topic. Hence, we consider more and more realistic problems. More precisely, we start our work by considering a centralized framework (i.e., the system is controlled by a single controller) and by synthesizing memoryless controllers (i.e., controllers that define their control policy from the current observation received from the system). Next, to obtain better solutions, we consider the synthesis of controllers that record a part or the whole of the execution of the system and use this information to define the control policy. Unfortunately, these methods cannot be used to control an interesting class of systems: the distributed systems. We have then defined methods that allow to control distributed systems with synchronous communications (decentralized and modular methods) and with asynchronous communications (distributed method). Moreover, we have implemented some of our algorithms to experimentally evaluate the quality of the synthesized controllers. / Un système à événements discrets est un système dont l'espace d'états est un ensemble discret et dont l'évolution de l'état courant dépend de l'occurrence d'événements discrets à travers le temps. Ces systèmes sont présents dans de nombreux domaines critiques tels les réseaux de communications, l'aéronautique, l'aérospatiale... La validité de ces systèmes est dès lors une question importante et une manière de l'assurer est d'utiliser des méthodes de contrôle supervisé. Ces méthodes associent au système un dispositif, appelé contrôleur, qui s'exécute en parrallèle et qui restreint le comportement du système de manière à empêcher qu'un comportement erroné ne se produise. Dans cette thèse, on s'intéresse au développement de méthodes de contrôle supervisé où le système peut avoir un espace d'états infini et où les contrôleurs ne sont pas toujours capables d'observer parfaitement le système; ce qui implique qu'ils doivent définir leur politique de contrôle à partir d'une connaissance imparfaite du système. Malheureusement, ce problème est généralement indécidable. Pour surmonter cette difficulté, nous utilisons alors des techniques d'interprétation abstraite qui assurent la terminaison de nos algorithmes au prix de certaines sur-approximations dans les calculs. Le but de notre thèse est de fournir la contribution la plus complète possible dans ce domaine et nous considèrons pour cela des problèmes de plus en plus réalistes. Plus précisement, nous avons commencé notre travail en définissant une méthode centralisée où le système est contrôlé par un seul contrôleur qui définit sa politique de contrôle à partir de la dernière information reçue du système. Ensuite, pour obtenir de meilleures solutions, nous avons défini des contrôleurs qui retiennent une partie ou la totalité de l'exécution du système et qui définissent leur politique de contrôle à partir de cette information. Malheureusement, ces méthodes ne peuvent pas être utilisées pour contrôler une classe intéressante de systèmes: les sytèmes distribués. Nous avons alors défini des méthodes permettant de contrôler des systèmes distribués dont les communications sont synchrones (méthodes décentralisées et modulaires) et asynchrones (méthodes distribuées). De plus, nous avons implémenté certains de nos algorithmes pour évaluer expérimentalement la qualité des contrôleurs qu'ils synthétisent.

Most of the past research on the problem of deadlock avoidance for complex resource allocation systems (RAS) has acknowledged the fact that the computation of the maximally permissive (optimal) deadlock avoidance policy (DAP) possesses super-polynomial complexity for most RAS classes, and therefore, it has resorted to solutions that trade off maximal permissiveness for computational tractability. In this work, we distinguish between the off-line and the on-line computation that is required for the effective implementation of the maximally permissive DAP, and we seek to develop representations of this policy that will require minimal on-line computation. The particular representation that we adopt is that of a compact classifier that will effect the underlying dichotomy of the reachable state space into safe and unsafe subspaces. Through a series of reductions of the derived classification problem, we are also able to attain extensive reductions in the computational complexity of the off-line task of the construction of the sought classifier. In a first study of the aforementioned problem, we restrict our attention to a particular RAS class that is motivated by an ongoing project called Gadara. This particular RAS class accepts the separation of the safe and unsafe subspaces of its instantiations through a set of linear inequalities. We propose design procedures that will construct a classifier employing the minimum possible number of linear inequalities, and we formally establish their "completeness", i.e., their ability to provide an effective classifier for every instance of the considered RAS class. We also offer heuristics that, if necessary, can alleviate the computational effort that is necessary for the construction of the sought classifier. We extend the aforementioned results to encompass more general RAS classes, where the sought dichotomy might not be represented by a set of linear inequalities. To this end, we propose new parametric and non-parametric classification schemes for this more complex case, and establish formally their completeness. We also provide effective and computationally efficient procedures for the synthesis of the sought classifiers. A bottleneck in the developments described above is defined by the fact that they presuppose the availability of the enumerations of the RAS safe and unsafe subspaces. To address this obstacle, we propose a novel approach for the deployment of the maximally permissive DAP for RAS, that is based on the identification and the efficient storage of a critical subset of states of the underlying RAS state space. In particular, the proposed algorithm provides those critical states, while avoiding the complete enumeration of the RAS state space. Furthermore, we extend the existing theory on maximally permissive deadlock avoidance, so that it can handle RAS with reader/writer (R/W) locks. A key challenge that is posed by this new RAS class stems from the fact that the underlying state space is not necessarily finite. We effectively address this obstacle by taking advantage of special structure that exists in the set of unsafe states and enables a finite representation of this set through its minimal elements. Finally, we would like to mention that numerical experimentation demonstrates the efficacy of the proposed approaches, and establishes their ability to support the deployment of maximally permissive DAP for RAS with very large structure and state spaces. To the best of our knowledge, these experiments also establish the ability of the proposed methodology to effectively compute tractable implementations of the maximally permissive DAP for problem instances significantly beyond the capacity of any other approach currently available in the literature. Moreover, this is the first work to address the RAS with R/W locks.

This thesis presents a supervisory hybrid controller for the automatic operation and control of a wind energy conversion and battery storage system. The supervisory hybrid control scheme is based on a radically different approach of modeling and control design, proposed for the subject wind energy conversion and battery storage system. The wind energy conversion unit is composed of a 360kW horizontal axis wind turbine mechanically coupled to an induction generator through a gearbox. The assembly is electrically interfaced to the dc bus through a thyristor-controlled rectifier to enable variable speed operation of the unit. Static capacitor banks have been used to meet reactive power requirements of the unit. A battery storage device is connected to the dc bus through a dc-dc converter to support operation of the wind energy conversion unit during islanded conditions. Islanding is assumed to occur when the tiebreaker to the utility feeder is in open position. The wind energy conversion unit and battery storage system is interfaced to the utility grid at the point of common coupling through a 25km long, 13.8kV feeder using a voltage-sourced converter unit. A bank of static (constant impedance) and dynamic (induction motor) loads is connected to the point of common coupling through a step down transformer. A finite hybrid-automata based model of the wind energy conversion and storage system has been proposed that captures the different operating regimes of the system during grid-connected and in islanded operating modes. The hybrid model of the subject system defines allowable operating states and predefines the transition paths between these operating states. A modular control design approach has been adapted in which the wind energy conversion and storage system has been partitioned along the dc bus into three independent system modules. Traditional control schemes using linear proportional-plus-integral compensators have been used for each system module with suitable modifications where necessary in order to achieve the required steady state and transient performance objectives. A supervisory control layer has been used to combine and configure control schemes of the three system modules to suite the requirements of system operation during any one operating state depicted by the hybrid model of the system. Transition management strategies have been devised and implemented through the supervisory control layer to ensure smooth inter-state transitions and bumpless switching among controllers. It has been concluded based on frequency domain linear analysis and time domain electromagnetic transient simulations that the proposed supervisory hybrid controller is capable of operating the wind energy conversion and storage system in both grid-connected and in islanded modes under changing operating conditions including temporary faults on the utility grid.

This thesis presents a supervisory hybrid controller for the automatic operation and control of a wind energy conversion and battery storage system. The supervisory hybrid control scheme is based on a radically different approach of modeling and control design, proposed for the subject wind energy conversion and battery storage system. The wind energy conversion unit is composed of a 360kW horizontal axis wind turbine mechanically coupled to an induction generator through a gearbox. The assembly is electrically interfaced to the dc bus through a thyristor-controlled rectifier to enable variable speed operation of the unit. Static capacitor banks have been used to meet reactive power requirements of the unit. A battery storage device is connected to the dc bus through a dc-dc converter to support operation of the wind energy conversion unit during islanded conditions. Islanding is assumed to occur when the tiebreaker to the utility feeder is in open position. The wind energy conversion unit and battery storage system is interfaced to the utility grid at the point of common coupling through a 25km long, 13.8kV feeder using a voltage-sourced converter unit. A bank of static (constant impedance) and dynamic (induction motor) loads is connected to the point of common coupling through a step down transformer. A finite hybrid-automata based model of the wind energy conversion and storage system has been proposed that captures the different operating regimes of the system during grid-connected and in islanded operating modes. The hybrid model of the subject system defines allowable operating states and predefines the transition paths between these operating states. A modular control design approach has been adapted in which the wind energy conversion and storage system has been partitioned along the dc bus into three independent system modules. Traditional control schemes using linear proportional-plus-integral compensators have been used for each system module with suitable modifications where necessary in order to achieve the required steady state and transient performance objectives. A supervisory control layer has been used to combine and configure control schemes of the three system modules to suite the requirements of system operation during any one operating state depicted by the hybrid model of the system. Transition management strategies have been devised and implemented through the supervisory control layer to ensure smooth inter-state transitions and bumpless switching among controllers. It has been concluded based on frequency domain linear analysis and time domain electromagnetic transient simulations that the proposed supervisory hybrid controller is capable of operating the wind energy conversion and storage system in both grid-connected and in islanded modes under changing operating conditions including temporary faults on the utility grid.

This thesis examines the design of supervisory controllers for windfarms of pitch-controlled wind turbines. The control objectives are the maximisation of the financial income from the generated electricity and the minimisation of the turbines' fatigue damage. The design exploits the wide variations in the ratio of financial income to fatigue damage which are found both spatially across windfarms and as a function of time. The supervisory control strategy makes use of the ability of pitch-controlled turbines to operate with variable power set points; a capability which is rarely exploited in practice. A windfarm simulation which has been developed for the purposes of testing supervisory controllers is described. It is shown that the simulation is a suitable test-bed for this application. Results are presented which demonstrate how the fatigue damage of a turbine's gearbox and structural components vary as functions of the mean wind-speed, turbulence intensity and power set point, both for isolated turbines and for turbines experiencing wake effects. A lifetime performance function is proposed and 'ideal' power set point curves are evaluated using a genetic search algorithm. It is shown that significant improvements in performance can be achieved if the operation of the turbines is altered to take account of variable electricity tariffs. A windfarm control strategy that splits the turbines into interacting and non-interacting categories is found to give good results. Using data generated by the simulation, it is shown that simple cost functions can be developed for non-interacting turbines which, when used in a controller, give performance that is close to the 'ideal'. A similar cost function is applied to a group of three interacting turbines, and it is found that substantial reductions in all measures of total annual fatigue damage are achieved for a small reduction in total annual financial income. The on-line implementation of windfarm supervisory controllers is discussed and the behaviour of a simple hill-climbing algorithm is examined using a simulated group of three interacting turbines.

Sistemas elétricos possuem grande influência no desenvolvimento econômico mundial. Dada a importância da energia elétrica para nossa sociedade, os sistemas elétricos frequentemente são alvos de intrusões pela rede causadas pelas mais diversas motivações. Para minimizar ou até mesmo mitigar os efeitos de intrusões pela rede, estão sendo propostos mecanismos que aumentam o nível de segurança dos sistemas elétricos, como novos protocolos de comunicação e normas de padronização. Além disso, os sistemas elétricos estão passando por um intenso processo de modernização, tornando-os altamente dependentes de sistemas de rede responsáveis por monitorar e gerenciar componentes elétricos. Estes, então denominados Smart Grids, compreendem subsistemas de geração, transmissão, e distribuição elétrica, que são monitorados e gerenciados por sistemas de controle e aquisição de dados (SCADA). Nesta dissertação de mestrado, investigamos e discutimos a aplicabilidade e os benefícios da adoção de Redes Definidas por Software (SDN) para auxiliar o desenvolvimento da próxima geração de sistemas SCADA. Propomos também um sistema de detecção de intrusões (IDS) que utiliza técnicas específicas de classificação de tráfego e se beneficia de características das redes SCADA e do paradigma SDN/OpenFlow. Nossa proposta utiliza SDN para coletar periodicamente estatísticas de rede dos equipamentos SCADA, que são posteriormente processados por algoritmos de classificação baseados em exemplares de uma única classe (OCC). Dado que informações sobre ataques direcionados à sistemas SCADA são escassos e pouco divulgados publicamente por seus mantenedores, a principal vantagem ao utilizar algoritmos OCC é de que estes não dependem de assinaturas de ataques para detectar possíveis tráfegos maliciosos. Como prova de conceito, desenvolvemos um protótipo de nossa proposta. Por fim, em nossa avaliação experimental, observamos a performance e a acurácia de nosso protótipo utilizando dois tipos de algoritmos OCC, e considerando eventos anômalos na rede SCADA, como um ataque de negação de serviço (DoS), e a falha de diversos dispositivos de campo. / Power grids have great influence on the development of the world economy. Given the importance of the electrical energy to our society, power grids are often target of network intrusion motivated by several causes. To minimize or even to mitigate the aftereffects of network intrusions, more secure protocols and standardization norms to enhance the security of power grids have been proposed. In addition, power grids are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this Masters dissertation, we investigate and discuss the applicability and benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also propose an Intrusion Detection System (IDS) that relies on specific techniques of traffic classification and takes advantage of the characteristics of SCADA networks and of the adoption of SDN/OpenFlow. Our proposal relies on SDN to periodically gather statistics from network devices, which are then processed by One- Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. As a proof-of-concept, we developed a prototype of our proposal. Finally, in our experimental evaluation, we observed the performance and accuracy of our prototype using two OCC-based Machine Learning (ML) algorithms, and considering anomalous events in the SCADA network, such as a Denial-of-Service (DoS), and the failure of several SCADA field devices.

A produção de frangos de corte vem assumindo papel de grande importância socioeconômica para o Brasil. Para o sucesso no desenvolvimento das aves, é fundamental um controle adequado de temperatura e umidade no interior do aviário, uma vez que o conforto térmico define a relação entre o consumo de ração e a produção de carne. Os métodos atuais de controle, comumente utilizados nos aviários brasileiros, não apresentam a eficiência desejável. Apesar de existirem dispositivos semiautomáticos, esses são dependentes da percepção e da intervenção do especialista humano, o que tende a ser ineficiente e propenso a erros. Esta pesquisa propõe uma abordagem integrada de controle, supervisão e suporte à tomada de decisões para aplicação no manejo avícola. Serão analisadas duas abordagens de controle apresentadas na literatura e então será proposta uma abordagem híbrida de integração. A primeira apresenta um controlador baseado na Teoria de Controle Supervisório (TCS), capaz de observar o conjunto de eventos da planta e orquestrar os atuadores de maneira minimamente restritiva, controlável, não bloqueante e de acordo com um conjunto de especificações. A segunda trata de um modelo de aprendizagem de máquina que, através de uma Rede Neuronal Artificial (RNA), fornece planos de ação para o manejo avícola, priorizando o bem-estar das aves e a produtividade. As duas abordagens serão examinadas e em seguida integradas, fornecendo ao operador três opções de gerenciamento do processo: controle puramente lógico (TCS); controle através da estratégia de inteligência artificial (RNA); e o controle lógico integrado aos planos de ação fornecidos pelo sistema especialista. Vencida a etapa de integração, é simulada uma situação real de manejo, e obtidas as respostas para as três opções de controle. Finalmente é apresentada uma análise comparativa entre os resultados obtidos por meio de cada abordagem. / The production of broilers has assumed a role of great socioeconomic importance for Brazil. For the success in the development of the poultry, an adequate control of temperature and humidity inside the aviary is essential, since the thermal comfort defines the relation between the feed consumption and the meat production. Current control methods, commonly used in Brazilian aviaries, do not have the desired efficiency. Although there are semiautomatic devices, they are dependent on the perception and intervention of the human expert, which tends to be inefficient and prone to errors. This research proposes an integrated approach to control, supervision and support to decision making for application in poultry management. Two control approaches presented in the literature will be analyzed and a hybrid integration approach will be proposed. The first one deals with a controller based on Supervisory Control Theory (TCS), able to observe the set of events of the plant and orchestrate the actuators in a minimally restrictive, controllable, non-blocking manner and according to a set of specifications. The second concerns a machine learning model that, through an artificial neural network (RNA), provides action plans for poultry management, prioritizing poultry welfare and productivity. The two approaches will be refined and then integrated, which will provide the operator with three process management options: purely logical control (TCS); control through artificial intelligence (RNA) strategy; and the logical control integrated to the action plans provided by the expert system. Once the integration stage is over, real management situations is= simulated, and responses are obtained for the three control options. Finally, a comparative analysis is presented between the results obtained through each approach.

CAPES / A geração de energia a partir de fontes alternativas é na atualidade tema de inúmeras pesquisas e desenvolvimento no meio científico e industrial. A energia eólica destaca-se nesse cenário como uma das mais proeminentes alternativas na geração de energia elétrica, por suas inúmeras vantagens. Em trabalhos de pesquisa, a reprodução computacional e em bancada do comportamento de uma turbina eólica, incluindo simulação de grandezas mecânicas e elétricas, que afetam diretamente a conversão de energia, são ferramentas muito apropriadas para o desenvolvimento e estudo de novas tecnologias quanto ao aproveitamento do potencial eólico de uma determinada região. Este trabalho aborda estudos referentes ao processo de conversão de energia em sistemas eólicos para geração de energia elétrica, com a finalidade de se desenvolver uma ferramenta para emulação de turbina eólica em bancada experimental, utilizando software LabVIEW®. O objetivo desta ferramenta é emular o torque mecânico desenvolvido no eixo de uma turbina eólica operando em geração distribuída, a partir de uma bancada experimental constituída por motor de indução trifásico e gerador síncrono a imã permanente, sob diferentes condições de velocidade de vento. Tal ferramenta possui o objetivo de ser flexível para demais arranjos laboratoriais, ou seja, capaz de ser empregada em outras estruturas de emulação de geração eólica em tempo real. Uma modelagem do sistema eólico é apresentada, desde a turbina ao gerador elétrico, após uma ferramenta de simulação em ambiente Matlab/Simulink® é desenvolvida com finalidade de pré-validar o experimento em bancada, então é realizada a implementação de projeto em bancada. / Power generation from alternative sources is at present the subject of numerous research and development in science and industry. Wind energy stands out in this scenario as one of the most prominent alternative in the generation of electricity, by its numerous advantages. In research works, computer reproduction and experimental behavior of a wind turbine are very suitable tools for the development and study of new technologies and the use of wind potential of a given region. These tools generally are desired to include simulation of mechanical and electrical parameters that directly affect the energy conversion. This work presents the energy conversion process in wind systems for power generation, in order to develop a tool for wind turbine emulation testing experimental, using LabVIEW® software. The purpose of this tool is to emulate the torque developed in an axis wind turbine. The physical setup consists of a three phase induction motor and a permanent magnet synchronous generator, which are evaluated under different wind speed conditions. This tool has the objective to be flexible to other laboratory arrangements, and can be used in other wind power generation structures in real time. A modeling of the wind power system is presented, from the turbine to the electrical generator. A simulation tool is developed using Matlab/Simulink® with the purpose to pre-validate the experiment setup. Finally, the design is implemented in a laboratory setup.