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Modelos neurais autônomos para classificação e localização de defeitos em linhas de transmissão
| Content Provider | Semantic Scholar |
|---|---|
| Author | Lopes, Daniel Do Souto |
| Copyright Year | 2017 |
| Abstract | The problem of fault diagnosis in transmission lines is one of the main challenges for the technical management of transmission facilities. The assertiveness on this activity is crucial to support decision making, reducing unavailability rates and promoting rapid reinstatement of the transmission function, contributing to the improvement of service quality and reducing the financial impacts arising from reductions in the variable portion. This document presents a proposal of intelligent system for classification and location of faults in transmission lines. The algorithms used are based on the so-called autonomous neural models which include analytical techniques for input selection and automatic structure specification without the need for an independent set of data for validation. Using Bayesian inference for specification and training of multilayer perceptrons (MLPs), the intelligent system provides probabilistic responses for classification of the type of fault and also for the distance of the fault from the monitored substation. Thus for the development of the models, technical data are used of a transmission line that is part of the National Interconnected System (SIN) which is modeled in an electromagnetic transient simulation software, ATP, aiming to establish the various fault scenarios. Furthermore, two types of equivalent network were analyzed, one detailed and one simple, in order to specify the best model and if there were significant differences in results in terms of fault representation. The databases with voltage and current oscillographs obtained for each type of fault are used for training and testing of the intelligent system, demonstrating the potential of the algorithms used. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://app.uff.br/riuff/bitstream/1/3932/1/Daniel%20Souto%20Disserta%C3%A7%C3%A3o.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
