Otimização de planos de manutenções de componentes de sistemas de distribuição de energia elétrica centrados em confiabilidade

Pós-graduação em Engenharia Elétrica - FEIS

A new probabilistic canopy dynamics model (SLCD) that is suitable for evergreen and deciduous forest ecosystems

There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change. While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the stand level on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable for enabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMs and G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrient fluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations from sparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) was designed to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and the total population size was limited depending on forest age and productivity. Foliage dynamics were driven by a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on the forest environment.The model was applied to three tree species growing under contrasting climates and soil types. In tropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameters on litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt stand were used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R-2 = 0.898 adjustment, R-2 = 0.698 validation). Litterfall production was correctly simulated (R-2 = 0.562, R-2 = 0.4018 validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA model to simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological patterns were simulated with good accuracy in the two cases studied. However, IA/max was not accurately simulated in the beech forest, and further improvement is required.Our probabilistic approach is expected to contribute to improving predictions of LAI dynamics. The model formalism is general and suitable to broadleaf forests for a large range of ecological conditions. (C) 2014 Elsevier B.V. All rights reserved.

Avaliação otimizada técnica e econômica do ponto de conexão de geração distribuída em redes de média tensão

Pós-graduação em Engenharia Elétrica - FEIS

Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Biogeography is the science that studies the geographical distribution and the migration of species in an ecosystem. Biogeography-based optimization (BBO) is a recently developed global optimization algorithm as a generalization of biogeography to evolutionary algorithm and has shown its ability to solve complex optimization problems. BBO employs a migration operator to share information between the problem solutions. The problem solutions are identified as habitat, and the sharing of features is called migration. In this paper, a multiobjective BBO, combined with a predator-prey (PPBBO) approach, is proposed and validated in the constrained design of a brushless dc wheel motor. The results demonstrated that the proposed PPBBO approach converged to promising solutions in terms of quality and dominance when compared with the classical BBO in a multiobjective version.

Evolving decision trees with beam search-based initialization and lexicographic multi-objective evaluation

Decision tree induction algorithms represent one of the most popular techniques for dealing with classification problems. However, traditional decision-tree induction algorithms implement a greedy approach for node splitting that is inherently susceptible to local optima convergence. Evolutionary algorithms can avoid the problems associated with a greedy search and have been successfully employed to the induction of decision trees. Previously, we proposed a lexicographic multi-objective genetic algorithm for decision-tree induction, named LEGAL-Tree. In this work, we propose extending this approach substantially, particularly w.r.t. two important evolutionary aspects: the initialization of the population and the fitness function. We carry out a comprehensive set of experiments to validate our extended algorithm. The experimental results suggest that it is able to outperform both traditional algorithms for decision-tree induction and another evolutionary algorithm in a variety of application domains.

Caracterización computacional de un detector de germanio hiperpuro de rango extendido (HPGe- XtRa) con simulación Montecarlo y optimización con un algoritmo evolutivo

Máster Universitario en Sistemas Inteligentes y Aplicaciones Numéricas en Ingeniería (SIANI)

Energy-Economical Heuristically Based Control of Compass Gait Walking on Stochastically Varying Terrain

Investigation uses simulation to explore the inherent tradeoffs ofcontrolling high-speed and highly robust walking robots while minimizing energy consumption. Using a novel controller which optimizes robustness, energy economy, and speed of a simulated robot on rough terrain, the user can adjust their priorities between these three outcome measures and systematically generate a performance curveassessing the tradeoffs associated with these metrics.

GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper's task of selecting a subset of the submitted bids which efficiently trades off total procurement cost against expected carrier performance. To solve this bi-objective winner determination problem, we propose a Pareto-based greedy randomized adaptive search procedure (GRASP). As a post-optimizer we use a path relinking procedure which is hybridized with branch-and-bound. Several variants of this algorithm are evaluated by means of artificial test instances which comply with important real-world characteristics. The two best variants prove superior to a previously published Pareto-based evolutionary algorithm.

EPANET Input Files of New York tunnels and Pacific City used in a metamodel-based optimization study

Metamodels have proven be very useful when it comes to reducing the computational requirements of Evolutionary Algorithm-based optimization by acting as quick-solving surrogates for slow-solving fitness functions. The relationship between metamodel scope and objective function varies between applications, that is, in some cases the metamodel acts as a surrogate for the whole fitness function, whereas in other cases it replaces only a component of the fitness function. This paper presents a formalized qualitative process to evaluate a fitness function to determine the most suitable metamodel scope so as to increase the likelihood of calibrating a high-fidelity metamodel and hence obtain good optimization results in a reasonable amount of time. The process is applied to the risk-based optimization of water distribution systems; a very computationally-intensive problem for real-world systems. The process is validated with a simple case study (modified New York Tunnels) and the power of metamodelling is demonstrated on a real-world case study (Pacific City) with a computational speed-up of several orders of magnitude.

Bio-inspired FPGA Architecture for Self-Calibration of an Image Compression Core based on Wavelet Transforms in Embedded Systems

A generic bio-inspired adaptive architecture for image compression suitable to be implemented in embedded systems is presented. The architecture allows the system to be tuned during its calibration phase. An evolutionary algorithm is responsible of making the system evolve towards the required performance. A prototype has been implemented in a Xilinx Virtex-5 FPGA featuring an adaptive wavelet transform core directed at improving image compression for specific types of images. An Evolution Strategy has been chosen as the search algorithm and its typical genetic operators adapted to allow for a hardware friendly implementation. HW/SW partitioning issues are also considered after a high level description of the algorithm is profiled which validates the proposed resource allocation in the device fabric. To check the robustness of the system and its adaptation capabilities, different types of images have been selected as validation patterns. A direct application of such a system is its deployment in an unknown environment during design time, letting the calibration phase adjust the system parameters so that it performs efcient image compression. Also, this prototype implementation may serve as an accelerator for the automatic design of evolved transform coefficients which are later on synthesized and implemented in a non-adaptive system in the final implementation device, whether it is a HW or SW based computing device. The architecture has been built in a modular way so that it can be easily extended to adapt other types of image processing cores. Details on this pluggable component point of view are also given in the paper.

Evolvable 2D computing matrix model for intrinsic evolution in commercial FPGAs with native reconfiguration support

This paper addresses the modelling and validation of an evolvable hardware architecture which can be mapped on a 2D systolic structure implemented on commercial reconfigurable FPGAs. The adaptation capabilities of the architecture are exercised to validate its evolvability. The underlying proposal is the use of a library of reconfigurable components characterised by their partial bitstreams, which are used by the Evolutionary Algorithm to find a solution to a given task. Evolution of image noise filters is selected as the proof of concept application. Results show that computation speed of the resulting evolved circuit is higher than with the Virtual Reconfigurable Circuits approach, and this can be exploited on the evolution process by using dynamic reconfiguration

Optimización de aperturas mediante técnicas de algoritmos evolutivos

Los sistemas de imagen por ultrasonidos son hoy una herramienta indispensable en aplicaciones de diagnóstico en medicina y son cada vez más utilizados en aplicaciones industriales en el área de ensayos no destructivos. El array es el elemento primario de estos sistemas y su diseño determina las características de los haces que se pueden construir (forma y tamaño del lóbulo principal, de los lóbulos secundarios y de rejilla, etc.), condicionando la calidad de las imágenes que pueden conseguirse. En arrays regulares la distancia máxima entre elementos se establece en media longitud de onda para evitar la formación de artefactos. Al mismo tiempo, la resolución en la imagen de los objetos presentes en la escena aumenta con el tamaño total de la apertura, por lo que una pequeña mejora en la calidad de la imagen se traduce en un aumento significativo del número de elementos del transductor. Esto tiene, entre otras, las siguientes consecuencias: Problemas de fabricación de los arrays por la gran densidad de conexiones (téngase en cuenta que en aplicaciones típicas de imagen médica, el valor de la longitud de onda es de décimas de milímetro) Baja relación señal/ruido y, en consecuencia, bajo rango dinámico de las señales por el reducido tamaño de los elementos. Complejidad de los equipos que deben manejar un elevado número de canales independientes. Por ejemplo, se necesitarían 10.000 elementos separados λ 2 para una apertura cuadrada de 50 λ. Una forma sencilla para resolver estos problemas existen alternativas que reducen el número de elementos activos de un array pleno, sacrificando hasta cierto punto la calidad de imagen, la energía emitida, el rango dinámico, el contraste, etc. Nosotros planteamos una estrategia diferente, y es desarrollar una metodología de optimización capaz de hallar de forma sistemática configuraciones de arrays de ultrasonido adaptados a aplicaciones específicas. Para realizar dicha labor proponemos el uso de los algoritmos evolutivos para buscar y seleccionar en el espacio de configuraciones de arrays aquellas que mejor se adaptan a los requisitos fijados por cada aplicación. En la memoria se trata el problema de la codificación de las configuraciones de arrays para que puedan ser utilizados como individuos de la población sobre la que van a actuar los algoritmos evolutivos. También se aborda la definición de funciones de idoneidad que permitan realizar comparaciones entre dichas configuraciones de acuerdo con los requisitos y restricciones de cada problema de diseño. Finalmente, se propone emplear el algoritmo multiobjetivo NSGA II como herramienta primaria de optimización y, a continuación, utilizar algoritmos mono-objetivo tipo Simulated Annealing para seleccionar y retinar las soluciones proporcionadas por el NSGA II. Muchas de las funciones de idoneidad que definen las características deseadas del array a diseñar se calculan partir de uno o más patrones de radiación generados por cada solución candidata. La obtención de estos patrones con los métodos habituales de simulación de campo acústico en banda ancha requiere tiempos de cálculo muy grandes que pueden hacer inviable el proceso de optimización con algoritmos evolutivos en la práctica. Como solución, se propone un método de cálculo en banda estrecha que reduce en, al menos, un orden de magnitud el tiempo de cálculo necesario Finalmente se presentan una serie de ejemplos, con arrays lineales y bidimensionales, para validar la metodología de diseño propuesta comparando experimentalmente las características reales de los diseños construidos con las predicciones del método de optimización. ABSTRACT Currently, the ultrasound imaging system is one of the powerful tools in medical diagnostic and non-destructive testing for industrial applications. Ultrasonic arrays design determines the beam characteristics (main and secondary lobes, beam pattern, etc...) which assist to enhance the image resolution. The maximum distance between the elements of the array should be the half of the wavelength to avoid the formation of grating lobes. At the same time, the image resolution of the target in the region of interest increases with the aperture size. Consequently, the larger number of elements in arrays assures the better image quality but this improvement contains the following drawbacks: Difficulties in the arrays manufacturing due to the large connection density. Low noise to signal ratio. Complexity of the ultrasonic system to handle large number of channels. The easiest way to resolve these issues is to reduce the number of active elements in full arrays, but on the other hand the image quality, dynamic range, contrast, etc, are compromised by this solutions In this thesis, an optimization methodology able to find ultrasound array configurations adapted for specific applications is presented. The evolutionary algorithms are used to obtain the ideal arrays among the existing configurations. This work addressed problems such as: the codification of ultrasound arrays to be interpreted as individuals in the evolutionary algorithm population and the fitness function and constraints, which will assess the behaviour of individuals. Therefore, it is proposed to use the multi-objective algorithm NSGA-II as a primary optimization tool, and then use the mono-objective Simulated Annealing algorithm to select and refine the solutions provided by the NSGA I I . The acoustic field is calculated many times for each individual and in every generation for every fitness functions. An acoustic narrow band field simulator, where the number of operations is reduced, this ensures a quick calculation of the acoustic field to reduce the expensive computing time required by these functions we have employed. Finally a set of examples are presented in order to validate our proposed design methodology, using linear and bidimensional arrays where the actual characteristics of the design are compared with the predictions of the optimization methodology.

Desarrollo de un Procedimiento Basado en Algoritmos de Optimización para el Dimensionado de Absorbedores Puntuales Aplicados a la Conversión de Energía Undimotriz

La energía transportada por el oleaje a través de los océanos (energía undimotriz) se enmarca dentro de las denominadas energías oceánicas. Su aprovechamiento para generar energía eléctrica (o ser aprovechada de alguna otra forma) es una idea reflejada ya hace más de dos siglos en una patente (1799). Desde entonces, y con especial intensidad desde los años 70, ha venido despertando el interés de instituciones ligadas al I+D+i y empresas del sector energético y tecnológico, debido principalmente a la magnitud del recurso disponible. Actualmente se puede considerar al sector en un estado precomercial, con un amplio rango de dispositivos y tecnologías en diferente grado de desarrollo en los que ninguno destaca sobre los otros (ni ha demostrado su viabilidad económica), y sin que se aprecie una tendencia a converger un único dispositivo (o un número reducido de ellos). El recurso energético que se está tratando de aprovechar, pese a compartir la característica de no-controlabilidad con otras fuentes de energía renovable como la eólica o la solar, presenta una variabilidad adicional. De esta manera, diferentes localizaciones, pese a poder presentar recursos de contenido energético similar, presentan oleajes de características muy diferentes en términos de alturas y periodos de oleaje, y en la dispersión estadística de estos valores. Esta variabilidad en el oleaje hace que cobre especial relevancia la adecuación de los dispositivos de aprovechamiento de energía undimotriz (WEC: Wave Energy Converter) a su localización, de cara a mejorar su viabilidad económica. Parece razonable suponer que, en un futuro, el proceso de diseño de un parque de generación undimotriz implique un rediseño (en base a una tecnología conocida) para cada proyecto de implantación en una nueva localización. El objetivo de esta tesis es plantear un procedimiento de dimensionado de una tecnología de aprovechamiento de la energía undimotriz concreta: los absorbedores puntuales. Dicha metodología de diseño se plantea como un problema de optimización matemático, el cual se resuelve utilizando un algoritmo de optimización bioinspirado: evolución diferencial. Este planteamiento permite automatizar la fase previa de dimensionado implementando la metodología en un código de programación. El proceso de diseño de un WEC es un problema de ingería complejo, por lo que no considera factible el planteamiento de un diseño completo mediante un único procedimiento de optimización matemático. En vez de eso, se platea el proceso de diseño en diferentes etapas, de manera que la metodología desarrollada en esta tesis se utilice para obtener las dimensiones básicas de una solución de referencia de WEC, la cual será utilizada como punto de partida para continuar con las etapas posteriores del proceso de diseño. La metodología de dimensionado previo presentada en esta tesis parte de unas condiciones de contorno de diseño definidas previamente, tales como: localización, características del sistema de generación de energía eléctrica (PTO: Power Take-Off), estrategia de extracción de energía eléctrica y concepto concreto de WEC). Utilizando un algoritmo de evolución diferencial multi-objetivo se obtiene un conjunto de soluciones factibles (de acuerdo con una ciertas restricciones técnicas y dimensionales) y óptimas (de acuerdo con una serie de funciones objetivo de pseudo-coste y pseudo-beneficio). Dicho conjunto de soluciones o dimensiones de WEC es utilizado como caso de referencia en las posteriores etapas de diseño. En el documento de la tesis se presentan dos versiones de dicha metodología con dos modelos diferentes de evaluación de las soluciones candidatas. Por un lado, se presenta un modelo en el dominio de la frecuencia que presenta importantes simplificaciones en cuanto al tratamiento del recurso del oleaje. Este procedimiento presenta una menor carga computacional pero una mayor incertidumbre en los resultados, la cual puede traducirse en trabajo adicional en las etapas posteriores del proceso de diseño. Sin embargo, el uso de esta metodología resulta conveniente para realizar análisis paramétricos previos de las condiciones de contorno, tales como la localización seleccionada. Por otro lado, la segunda metodología propuesta utiliza modelos en el domino estocástico, lo que aumenta la carga computacional, pero permite obtener resultados con menos incertidumbre e información estadística muy útil para el proceso de diseño. Por este motivo, esta metodología es más adecuada para su uso en un proceso de dimensionado completo de un WEC. La metodología desarrollada durante la tesis ha sido utilizada en un proyecto industrial de evaluación energética preliminar de una planta de energía undimotriz. En dicho proceso de evaluación, el método de dimensionado previo fue utilizado en una primera etapa, de cara a obtener un conjunto de soluciones factibles de acuerdo con una serie de restricciones técnicas básicas. La selección y refinamiento de la geometría de la solución geométrica de WEC propuesta fue realizada a posteriori (por otros participantes del proyecto) utilizando un modelo detallado en el dominio del tiempo y un modelo de evaluación económica del dispositivo. El uso de esta metodología puede ayudar a reducir las iteraciones manuales y a mejorar los resultados obtenidos en estas últimas etapas del proyecto. ABSTRACT The energy transported by ocean waves (wave energy) is framed within the so-called oceanic energies. Its use to generate electric energy (or desalinate ocean water, etc.) is an idea expressed first time in a patent two centuries ago (1799). Ever since, but specially since the 1970’s, this energy has become interesting for R&D institutions and companies related with the technological and energetic sectors mainly because of the magnitude of available energy. Nowadays the development of this technology can be considered to be in a pre-commercial stage, with a wide range of devices and technologies developed to different degrees but with none standing out nor economically viable. Nor do these technologies seem ready to converge to a single device (or a reduce number of devices). The energy resource to be exploited shares its non-controllability with other renewable energy sources such as wind and solar. However, wave energy presents an additional short-term variability due to its oscillatory nature. Thus, different locations may show waves with similar energy content but different characteristics such as wave height or wave period. This variability in ocean waves makes it very important that the devices for harnessing wave energy (WEC: Wave Energy Converter) fit closely to the characteristics of their location in order to improve their economic viability. It seems reasonable to assume that, in the future, the process of designing a wave power plant will involve a re-design (based on a well-known technology) for each implementation project in any new location. The objective of this PhD thesis is to propose a dimensioning method for a specific wave-energy-harnessing technology: point absorbers. This design methodology is presented as a mathematical optimization problem solved by using an optimization bio-inspired algorithm: differential evolution. This approach allows automating the preliminary dimensioning stage by implementing the methodology in programmed code. The design process of a WEC is a complex engineering problem, so the complete design is not feasible using a single mathematical optimization procedure. Instead, the design process is proposed in different stages, so the methodology developed in this thesis is used for the basic dimensions of a reference solution of the WEC, which would be used as a starting point for the later stages of the design process. The preliminary dimensioning methodology presented in this thesis starts from some previously defined boundary conditions such as: location, power take-off (PTO) characteristic, strategy of energy extraction and specific WEC technology. Using a differential multi-objective evolutionary algorithm produces a set of feasible solutions (according to certain technical and dimensional constraints) and optimal solutions (according to a set of pseudo-cost and pseudo-benefit objective functions). This set of solutions or WEC dimensions are used as a reference case in subsequent stages of design. In the document of this thesis, two versions of this methodology with two different models of evaluation of candidate solutions are presented. On the one hand, a model in the frequency domain that has significant simplifications in the treatment of the wave resource is presented. This method implies a lower computational load but increased uncertainty in the results, which may lead to additional work in the later stages of the design process. However, use of this methodology is useful in order to perform previous parametric analysis of boundary conditions such as the selected location. On the other hand, the second method uses stochastic models, increasing the computational load, but providing results with smaller uncertainty and very useful statistical information for the design process. Therefore, this method is more suitable to be used in a detail design process for full dimensioning of the WEC. The methodology developed throughout the thesis has been used in an industrial project for preliminary energetic assessment of a wave energy power plant. In this assessment process, the method of previous dimensioning was used in the first stage, in order to obtain a set of feasible solutions according to a set of basic technical constraints. The geometry of the WEC was refined and selected subsequently (by other project participants) using a detailed model in the time domain and a model of economic evaluation of the device. Using this methodology can help to reduce the number of design iterations and to improve the results obtained in the last stages of the project.

On constructing folding heteropolymers.

Simplified models of the protein-folding process have led to valuable insights into the generic properties of the folding of heteropolymers. On the basis of theoretical arguments, Shakhnovich and Gutin [(1993) Proc. Natl. Acad. Sci. USA 90, 7195-7199] have proposed a specific method to generate folding sequences for one of these. Here we present a model of folding in heteropolymers that is comparable in simplicity but different in spirit to the one studied by Shakhnovich and Gutin. In our model, the proposed recipe for constructing folding sequence fails. We find that, as a rule, the construction of folding sequences is impossible to achieve by looking at the native conformation only. Rather, competing conformations have to be taken into account too. An evolutionary algorithm that generates folding sequences by optimizing both stability of the native state and folding time is described. Remarkably, this algorithm produces, among others, sequences that fold reproducibly to metastable states.

Planejamento de sistemas de distribuição de energia elétrica considerando questões de confiabilidade e risco

O problema de Planejamento da Expansão de Sistemas de Distribuição (PESD) visa determinar diretrizes para a expansão da rede considerando a crescente demanda dos consumidores. Nesse contexto, as empresas distribuidoras de energia elétrica têm o papel de propor ações no sistema de distribuição com o intuito de adequar o fornecimento da energia aos padrões exigidos pelos órgãos reguladores. Tradicionalmente considera-se apenas a minimização do custo global de investimento de planos de expansão, negligenciando-se questões de confiabilidade e robustez do sistema. Como consequência, os planos de expansão obtidos levam o sistema de distribuição a configurações que são vulneráveis a elevados cortes de carga na ocorrência de contingências na rede. Este trabalho busca a elaboração de uma metodologia para inserir questões de confiabilidade e risco ao problema PESD tradicional, com o intuito de escolher planos de expansão que maximizem a robustez da rede e, consequentemente, atenuar os danos causados pelas contingências no sistema. Formulou-se um modelo multiobjetivo do problema PESD em que se minimizam dois objetivos: o custo global (que incorpora custo de investimento, custo de manutenção, custo de operação e custo de produção de energia) e o risco de implantação de planos de expansão. Para ambos os objetivos, são formulados modelos lineares inteiros mistos que são resolvidos utilizando o solver CPLEX através do software GAMS. Para administrar a busca por soluções ótimas, optou-se por programar em linguagem C++ dois Algoritmos Evolutivos: Non-dominated Sorting Genetic Algorithm-2 (NSGA2) e Strength Pareto Evolutionary Algorithm-2 (SPEA2). Esses algoritmos mostraram-se eficazes nessa busca, o que foi constatado através de simulações do planejamento da expansão de dois sistemas testes adaptados da literatura. O conjunto de soluções encontradas nas simulações contém planos de expansão com diferentes níveis de custo global e de risco de implantação, destacando a diversidade das soluções propostas. Algumas dessas topologias são ilustradas para se evidenciar suas diferenças.