Localização de faltas de curta duração em redes de distribuição.

• Autor(a) principal: Moraes, Tiago Fernandes
• Data de Publicação: 2014
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/11824
• topic: Electronic Engineering; Artificial neural network; Short Duration Voltage Variations (SDVV); Phase-Locked Loop (PLL); Electrical power distribution system; PSCAD/EMTDC; Engenharia Eletrônica; Redes neurais artificiais; Variação de Tensão de Curta Duração (VTCD); Sistema de distribuição de energia elétrica, PSCAD/EMTDC; CNPQ::ENGENHARIAS

The aim of this work is to contribute to the development of a technique based on intelligent systems that allows the accurate location of the Short Duration Voltage Variations (SDVV) origin in an electrical power distribution system. Once the fault is detected via a Phase-Locked Loop (PLL), voltage signals acquired during the fault are decomposed into instantaneous symmetrical components by the proposed method. Then, the energies of the symmetrical components are calculated and used to estimate the fault location. In this work, two systems based on Artificial Neural Networks (ANN) are evaluated. The first one is designed to classify the fault location into one of predefined possible points and the second is designed to estimate the fault distance from the feeder. The technique herein proposed is applies to three-phase feeders with balanced loads. In addition, it is considered that there is availability of voltage measurements in the initial node of the feeder and also in sparse points along the distribution power grid. The employed database was made using simulations of a model of radial feeder using the PSCAD / EMTDC program. Sensitivity tests employing cross-validation are performed for both approaches in order to verify the reliability of the results. Furthermore, in order to check the generalization capability, tests with faults not originally contained in the database were performed. The performances of both architectures of neural networks were satisfactory and they demonstrate the feasibility of the proposed techniques to perform fault location on distribution grids.