Controlo clássico vs. preditivo na regulação de frequência em sistemas de potência

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial

REXIB: Remote Experiments Interface Builder

Remote Experimentation is an educational resource that allows teachers to strengthen the practical contents of science & engineering courses. However, building up the interfaces to remote experiments is not a trivial task. Although teachers normally master the practical contents addressed by a particular remote experiment they usually lack the programming skills required to quickly build up the corresponding web interface. This paper describes the automatic generation of experiment interfaces through a web-accessible Java application. The application displays a list of existent modules and once the requested modules have been selected, it generates the code that enables the browser to display the experiment interface. The tools? main advantage is enabling non-tech teachers to create their own remote experiments.

Metop-A generator for single top production via FCNC interactions

We present a generator for single top-quark production via flavour-changing neutral currents. The MEtop event generator allows for Next-to-Leading-Order direct top production pp -> t and Leading-Order production of several other single top processes. A few packages with definite sets of dimension six operators are available. We discuss how to improve the bounds on the effective operators and how well new physics can be probed with each set of independent dimension six operators.

On performance of distributed model predictive control in power system frequency regulation

This paper describes the implementation of a distributed model predictive approach for automatic generation control. Performance results are discussed by comparing classical techniques (based on integral control) with model predictive control solutions (centralized and distributed) for different operational scenarios with two interconnected networks. These scenarios include variable load levels (ranging from a small to a large unbalance generated power to power consumption ratio) and simultaneously variable distance between the interconnected networks systems. For the two networks the paper also examines the impact of load variation in an island context (a network isolated from each other).

Analysable software language translations

Dissertação para obtenção do Grau de Doutor em Engenharia Informática

Instrumental Fado: a generative interactive system

Fado was listed as UNESCO Intangible Cultural Heritage in 2011. This dissertation describes a theoretical model, as well as an automatic system, able to generate instrumental music based on the musics and vocal sounds typically associated with fado’s practice. A description of the phenomenon of fado, its musics and vocal sounds, based on ethnographic, historical sources and empirical data is presented. The data includes the creation of a digital corpus, of musical transcriptions, identified as fado, and statistical analysis via music information retrieval techniques. The second part consists in the formulation of a theory and the coding of a symbolic model, as a proof of concept, for the automatic generation of instrumental music based on the one in the corpus.

SOFHIA: a CAD environment to design digital control systems

"Series Title: IFIP - The International Federation for Information Processing, ISSN 1868-4238"

An evolutionary approach to the use of Petri net based models: from parallel controllers to HW/SW co-design

"A workshop within the 19th International Conference on Applications and Theory of Petri Nets - ICATPN’1998"

Generació automàtica d'una solució JEE (JPA + EJB + JSF) basada en el framework AndroMDA

En aquest projecte s'utilitzarà el framework AndroMDA per construir un programari que permeti generar automàticament a partir d'un model UML simple una aplicació JEE.

Estudi d'eines CASE que suporten OCLper a la generació automàtica de codi Java

Es tracta d'una recerca d'eines CASEque actualment suporten OCL en la generació automàtica de codi Java per estudiar-les ianalitzar-les a través d'un model de proves consistent en un diagrama de classes del modelestàtic de l'UML i una mostra variada d'instruccions OCL, amb l'objectiu de detectar lesseves mancances, analitzant el codi obtingut i determinar si controla o no cada tipus derestricció, i si s'han implementat bé en el codi.

Generació automàtica de codi Java amb ArgoUML

Aquesta memòria presenta un estudi de la generació automàtica de codi Java a partir de diagrames UML amb l'eina ArgoUML. El cicle de vida tradicional del programari presenta alguns problemes com la manca de sincronització entre codi i documentació, poca portabilitat i problemes de interoperabilidad. El paradigma MDA vol solucionar en part aquests problemes fent dels models el centre del desenvolupament del programari i apostant per la generació automàtica de models i codi.

J2EE : Generació automàtica de codi

Aquest projecte està enfocat a determinar l'estat actual de les principals eines de generació automàtica de codi que existeixen, analitzant les característiques principals de cada eina determinar-ne les funcionalitats.

Generació automàtica de les constraints a partir de un diagrama de classes UML amb restriccions especificades en OCL

L'àmbit d'aquest treball és la generació automàtica de les restriccions d'integritat (claus primàries, alternatives i comprovacions), tant per a les bases de dades relacionals com per a les orientades a objectes.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Realismo 3D aplicado a la Gestión Municipal

La visualización 3D ofrece una serie de ventajas y funcionalidades cada vez más demandadas, por lo que es conveniente su incorporación a las aplicaciones GIS. El sistema propuesto integra la vista 2D propia de un GIS y la vista 3D garantizando la interacción entre ellas y teniendo por resultado una solución GIS integral. Se permite la carga de Modelos Digitales del Terreno (MDT), de forma directa o empleando servicios OGC-CSW, para la proyección de los elementos 2D, así como la carga de modelos 3D. Además el sistema está dotado de herramientas para extrusión y generación automática de volúmenes empleando parámetros existentes en la información 2D. La generación de las construcciones a partir de su altura y la elaboración de redes tridimensionales a partir de la profundidad en las infraestructuras son algunos casos prácticos de interés. Igualmente se permite no sólo la consulta y visualización sino también la edición 3D, lo que supone una importante ventaja frente a otros sistemas 3D. LocalGIS, Sistema de Información Territorial de software libre aplicado a la gestión municipal, es el sistema GIS empleado para la incorporación del prototipo. Se permite por lo tanto aplicar todas las ventajas y funcionalidades propias del 3D a la gestión municipal que LocalGIS realiza. Esta tecnología ofrece un campo de aplicaciones muy amplio y prometedor