Durante décadas de investigación en problemas de optimización, se han desarrollado innumerables algoritmos, tanto determinísticos como heurísticos. Aun así, debido a este amplio abanico de posibilidades resulta una tarea compleja determinar cuál de ellos es más adecuado para un problema específico. Se propone en este trabajo una comparativa entre diferentes algoritmos heurísticos de optimización haciendo uso de test estadísticos. Se consideró Simulated Annealing (SA), Algoritmo Genético Simple (sGA), Algoritmos Genético Compacto (cGA) y Deterministic Crowding (DC) aplicados al problema de Selección de Variables para Estimación utilizando Regresión Lineal. Se utilizaron tres casos de prueba, que consisten en la estimación de Radiación Solar, Consumo Eléctrico en la provincia de Tucumán (Argentina), y el tiempo de reaparición de células cancerígenas. Se comprobó que existe suficiente evidencia estadística para afirmar que los algoritmos arrojan resultados significativamente diferentes. Además se concluye que sGA y DC son los algoritmos más apropiados para estos problemas, permitiendo obtener valores de fitness similares siendo sGA ligeramente superior.

Variable selection problems generally present more than a single solution and, sometimes, it is worth to find as many solutions as possible. The use of Evolutionary Algorithms applied to this kind of problem proves to be one of the best methods to find optimal solutions. Moreover, there are variants designed to find all or almost all local optima, known as Niching Genetic Algorithms (NGA). There are several different NGA methods developed in order to achieve this task. The present work compares the behavior of eight different niching techniques, applied to a climatic database of four weather stations distributed in Tucuman, Argentina. The goal is to find different sets of input variables that have been used as the input variable by the estimation method. Final results were evaluated based on low estimation error and low dispersion error, as well as a high number of different results and low computational time. A second experiment was carried out to study the capability of the method to identify critical variables. The best results were obtained with Deterministic Crowding. In contrast, Steady State Worst Among Most Similar and Probabilistic Crowding showed good results but longer processing times and less ability to determine the critical factors.

La presente invención pertenece al campo técnico de la medición de variables y análisis de datos en sistemas de provisión y distribución de energía eléctrica en general, en particular, enfocado a la identificación de fase y vinculación con la Subestación Transformadora de clientes en redes de distribución de baja tensión y Microgrids. La misma se refiere a un método para la identificación de fases y la vinculación con transformadores en redes de distribución de electricidad de baja tensión, basado en un procedimiento estocástico de optimización combinatoria, el cual requiere mediciones sincronizadas de la energía consumida por cada fase de los Nodos Fuente de Energía (distribuidores) y de la energía consumida individualmente por al menos un 10% de los Nodos Consumidores de Energía (clientes) conectados a una Microgrid, donde al menos 70% de dichos Nodos Consumidores de Energía reciben energía de dicho Nodo Fuente de Energía y es resistente a falta de datos.

SARL is a general-purpose agent-oriented program- ming language. This language aims at providing the fundamental abstractions for dealing with concurrency, distribution, interac- tion, decentralization, reactivity, autonomy and dynamic reconfig- uration that are usually considered as essential for implementing agent- based applications. Every programming language specifies an execution model. In the case of SARL, this execution model is defined based upon the object-oriented paradigm, e.g. a run-time environment written with Java. Accordingly, and by default, the SARL programs are transformed into their equivalent object- oriented programs. The goal of this paper is the explanation of the mapping between the agent paradigm and the object- oriented paradigm, and the definition of transformations from the SARL constructs to the standard object-oriented constructs. They enable the SARL developer understanding the SARL statements, and the mapping to executable entities. The transformations in this paper could also serve as the basis for creating a compiler ex- tension for targeting any object-oriented programming language.

Currently, the electrical system in Argentina is working at its maximum capacity, decreasing the margin between the installed power and demanded consumption, and drastically reducing the service life of transformer substations due to overload (since the margin for summer peaks is small). The advent of the Smart Grids allows electricity distribution companies to apply data analysis techniques to manage resources more efficiently at different levels (avoiding damages, better contingency management, maintenance planning, etc.). The Smart Grids in Argentina progresses slowly due to the high costs involved. In this context, the estimation of the lifespan reduction of distribution transformers is a key tool to efficiently manage human and material resources, maximizing the lifetime of this equipment. Despite the current state of the smart grids, the electricity distribution companies can implement it using the available data. Thermal models provide guidelines for lifespan estimation, but the adjustment to particular conditions, brands, or material quality is done by adjusting parameters. In this work we propose a method to adjust the parameters of a thermal model using Genetic Algorithms, comparing the estimation values of top-oil temperature with measurements from 315 kVA distribution transformers, located in the province of Tucumán, Argentina. The results show that, despite limited data availability, the adjusted model is suitable to implement a transformer monitoring system.