Abstract

Abstract
A localização de uma nova subestação é um fator chave na expansão dos sistemas de distribuição de energia elétrica. Assim, esse local é estratégico do ponto de vista dos custos associados ao suprimento de energia, devendo-se considerar um planejamento holístico e integral dos subsistemas de subtransmissão e distribuição primaria. Embora o crescimento futuro da carga seja um fator determinante para sua capacidade e localização, foram identificados outros critérios técnicos, ambientais, características do solo, risco, critérios sociais, e administrativos que influenciam a localização final. Uma análise de decisão multicritério, baseada em sistemas de informações geográficas, é usada para combinar esses critérios, considerando as preferências dos tomadores de decisão e as restrições físicas do uso da terra. As principais contribuições deste trabalho são a identificação dos critérios, dentre os quais se destaca uma análise de densidade de carga baseada na previsão espaço-temporal da demanda. O produto dessa análise, chamadode adequação, é um mapa de decisão para identificar locais ideais para novas subestações. O desempenho do método proposto é avaliado na área de serviço de uma concessionária de energia equatoriana, o que agrega mais valor a essa proposta, com o intuito que possa ser aplicada em casos reais.

Abstract
Domestic energy policies destined to foster the use of end-use electric technologies could cause rapid penetration of new residential loads and, consequently, this could cause a significant increase in the demand for electricity in urban areas. This paper presents a spatial-temporal growth model for estimating the adoption of new end-use electric technologies encouraged by energy-efficiency policies. The proposed method consists of three modules: temporal, spatial and grouping. The temporal module calculates by districts or census tracts of a city, the percentage of homes in which residents are prospective buyers of a new end-use electric technology. Then, the spatial module adjusts the calculations made by the temporal module, considering the spatial interactions among the inhabitants of the districts. Finally, the grouping module discovers the low-voltage transformer where the prospective buyers are connected. The results of the proposed model are a spatial database with information related to the percentage of homes in which residents are prospective buyers of a new end-use electric technology, as well as the number of prospective buyers connected to each low-voltage transformer. The results can visualize through thematic maps to identify the districts where the new technology will have faster adoption. The proposed method was employed to estimate the adoption of induction heating cookers in a medium-sized Ecuadorian city. The Ecuadorian government has developed a program of economic subsidies to encourage its population to use this electrical appliance. The results from this application are an important tool to estimate the spatial increase in electricity demand, decide important issues related to the planning of distributed resources, and develop demand-side management programs. Furthermore, the results can be used to evaluate and manage energy policies formulated to achieve environmental and energy goals.

Abstract
En este documento se presenta una evaluación experimental del estado del aislamiento de un transformador trifásico de 50 kVA sumergido en aceite a través de la aplicación de impulsos atmosféricos estándar de alto voltaje y su respuesta en frecuencia realizando un Análisis de la Respuesta en Frecuencia al Impulso (IFRA). La evaluación se basa en tener dos estados representativos del aislamiento, por ejemplo, cuando el transformador usa aceite dieléctrico deteriorado y luego este aceite es reemplazado por uno nuevo, por lo que se realiza el cambio del aceite dieléctrico. Se lleva a cabo una valoración general del estado del transformador en ambos estados representativos mediante la ejecución de pruebas de rutina. El análisis de la respuesta en frecuencia se desarrolla mediante la obtención de la respuesta del transformador en los dos estados representativos del aislamiento y bajo diferentes configuraciones de medición. Posteriormente se evalúa a través de una comparación gráfica entre las respuestas en frecuencia, valorando cualitativamente y cuantitativamente las figuras obtenidas; y relacionándolas con el estado del aislamiento. Por ello, se proporciona una metodología experimental como una opción al uso de equipo especializado para análisis de respuesta en frecuencia por medio de la acción de impulsos atmosféricos estándar de alto voltaje, usando el equipo disponible en el Laboratorio de Alto Voltaje de la Escuela Politécnica Nacional destinado para pruebas de impulso.

Abstract
This paper presents qualitative and quantitative analysis of the traveling waves induced by faults on direct current (DC) transmission lines of line-commutated converter high-voltage direct current (LCC HVDC) systems for detecting the wavefront arrival times using the boundary wavelet coefficients from real-time stationary wavelet transform (RT-SWT). The qualitative analysis takes into account the steady-state operation and the detection of the inception times of both first and second wavefronts at the converter stations. The behavior of the boundary wavelet coefficients in DC transmission lines is examined considering the effects of the main parameters that influence the detection of the traveling waves, such as mother wavelets, sampling frequency, DC transmission line terminations, electrical noises, as well as fault resistance and distance. An algorithm designed to run in real-time and able to minimize the factors that hamper the performance of traveling wave-based protection (TWP) methods is proposed to detect the first and second surge arrival times. Quantitative results are achieved based on the accuracy of one- and two-terminal fault location estimation methods, and indicate the proper operation of the presented algorithm.

Abstract
This paper presents a new method to identify classes of events, by processing phasor measurement units (PMU) frequency data through deep neural networks. Deep tapered Multi-layer Perceptrons of the half-autoencoder type, Deep Belief Networks and Convolutional Neural Networks (CNN) are compared, using real data from Brazil. A sound success is obtained by a transformation of time-domain signals, from dynamic events recorded, into 2D images; these images wee processed with a CNN, taking advantage of the strong dependency existing among neighboring pixels in images. The training, computing and processing was achieved with a GPU (Graphics Processing Unit), allowing speeding-up of the process up to 30 times and rendering the process suitable to increase the online situational awareness of network operators.