Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

Introduction to the special issue on real world applications of intelligent tutoring systems

The idea behind creating this special issue on real world applications of intelligent tutoring systems was to bring together in a single publication some of the most important examples of success in the use of ITS technology. This will serve as a reference to all researchers working in the area. It will also be an important resource for the industry, showing the maturity of ITS technology and creating an atmosphere for funding new ITS projects. Simultaneously, it will be valuable to academic groups, motivating students for new ideas of ITS and promoting new academic research work in the area.

Real time manufacturing systems simulator

A área da simulação computacional teve um rápido crescimento desde o seu apareciment, sendo actualmente uma das ciências de gestão e de investigação operacional mais utilizadas. O seu princípio baseia-se na replicação da operação de processos ou sistemas ao longo de períodos de tempo, tornando-se assim uma metodologia indispensável para a resolução de variados problemas do mundo real, independentemente da sua complexidade. Das inúmeras áreas de aplicação, nos mais diversos campos, a que mais se destaca é a utilização em sistemas de produção, onde o leque de aplicações disponível é muito vasto. A sua aplicação tem vindo a ser utilizada para solucionar problemas em sistemas de produção, uma vez que permite às empresas ajustar e planear de uma maneira rápida, eficaz e ponderada as suas operações e os seus sistemas, permitindo assim uma rápida adaptação das mesmas às constantes mudanças das necessidades da economia global. As aplicações e packages de simulação têm seguindo as tendências tecnológicas pelo que é notório o recurso a tecnologias orientadas a objectos para o desenvolvimento das mesmas. Este estudo baseou-se, numa primeira fase, na recolha de informação de suporte aos conceitos de modelação e simulação, bem como a respectiva aplicação a sistemas de produção em tempo real. Posteriormente centralizou-se no desenvolvimento de um protótipo de uma aplicação de simulação de ambientes de fabrico em tempo real. O desenvolvimento desta ferramenta teve em vista eventuais fins pedagógicos e uma utilização a nível académico, sendo esta capaz de simular um modelo de um sistema de produção, estando também dotada de animação. Sem deixar de parte a possibilidade de integração de outros módulos ou, até mesmo, em outras plataformas, houve ainda a preocupação acrescida de que a sua implementação recorresse a metodologias de desenvolvimento orientadas a objectos.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

TINSEL – a web-based intelligent tutoring system on electrical installation design

This paper presents a project consisting on the development of an Intelligent Tutoring System, for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students. One of the major goals of this project is to devise a teaching model based on Intelligent Tutoring techniques, considering not only academic knowledge but also other types of more empirical knowledge, able to achieve successfully the training of electrical installation design.

An intelligent tutoring approach to support students and technicians in electrical installation design

Designing electric installation projects, demands not only academic knowledge, but also other types of knowledge not easily acquired through traditional instructional methodologies. A lot of additional empirical knowledge is missing and so the academic instruction must be completed with different kinds of knowledge, such as real-life practical examples and simulations. On the other hand, the practical knowledge detained by the most experienced designers is not formalized in such a way that is easily transmitted. In order to overcome these difficulties present in the engineers formation, we are developing an Intelligent Tutoring System (ITS), for training and support concerning the development of electrical installation projects to be used by electrical engineers, technicians and students.

Intelligent behavior in a Cyber-Ambient training system for control center operators

This paper deals with the application of an intelligent tutoring approach to delivery training in diagnosis procedures of a Power System. In particular, the mechanisms implemented by the training tool to support the trainees are detailed. This tool is part of an architecture conceived to integrate Power Systems tools in a Power System Control Centre, based on an Ambient Intelligent paradigm. The present work is integrated in the CITOPSY project which main goal is to achieve a better integration between operators and control room applications, considering the needs of people, customizing requirements and forecasting behaviors.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Using data mining techniques to support DR programs definition in smart grids

In recent years, Power Systems (PS) have experimented many changes in their operation. The introduction of new players managing Distributed Generation (DG) units, and the existence of new Demand Response (DR) programs make the control of the system a more complex problem and allow a more flexible management. An intelligent resource management in the context of smart grids is of huge important so that smart grids functions are assured. This paper proposes a new methodology to support system operators and/or Virtual Power Players (VPPs) to determine effective and efficient DR programs that can be put into practice. This method is based on the use of data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 32 bus distribution network.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Simulation and dynamics of freeway traffic

This paper presents the new package entitled Simulator of Intelligent Transportation Systems (SITS) and a computational oriented analysis of traffic dynamics. The SITS adopts a microscopic simulation approach to reproduce real traffic conditions considering different types of vehicles, drivers and roads. A set of experiments with the SITS reveal the dynamic phenomena exhibited by this kind of system. For this purpose a modelling formalism is developed that embeds the statistics and the Laplace transform. The results make possible the adoption of classical system theory tools and point out that it is possible to study traffic systems taking advantage of the knowledge gathered with automatic control algorithms. A complementary perspective for the analysis of the traffic flow is also quantified through the entropy measure.

Estudo do processo de fabricação de perfis híbridos pultrudidos

A realização deste trabalho teve como principal objectivo proceder ao projecto e realização de dois sistemas de produção de perfis pultrudidos híbridos a acoplar aos equipamentos de pultrusão já instalados na ALTO – PERFIS PULTRUDIDOS, Lda. A realização de perfis pultrudidos híbridos visa melhorar as características de isolamento térmico e acústico dos tubos habitualmente feitos por pultrusão, incrementando simultaneamente a sua resistência, através da melhoria do momento de inércia do perfil, sem que haja um aumento significativo do peso do conjunto. O projecto teve como base de trabalho dois sistemas de produção completamente distintos: (a) a existência de barras de cortiça e de poliuretano (pré-formas) sobre as quais se vai efectuar a pultrusão de um tubo que abraça as barras e (b) o uso de resíduos de pultrusão como forma de enchimento dos tubos, durante o seu processo produtivo. Estes equipamentos foram projectados e parcialmente fabricados, antevendo-se que os mesmos entrem em funcionamento muito brevemente.

Probabilistic Egomotion for Stereo Visual Odometry

We present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of vehicle’s angular and linear velocities. To validate our approach, we perform two sets of experiments with a well known benchmarking dataset. First, we assess the quality of the raw velocity estimates in comparison to classical pose estimation algorithms. Second, we added to our method’s instantaneous velocity estimates a Kalman Filter and compare its performance with a well known open source stereo Visual Odometry library. The presented results compare favorably with state-of-the-art approaches, mainly in the estimation of the angular velocities, where significant improvements are achieved.