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Your search keyword '"Bonaldi, Erik Leandro"' showing total 24 results
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24 results on '"Bonaldi, Erik Leandro"'

1. On the Use of Vibration Analysis for Contact Fault Detection in High-Voltage HVCBs

Author

Reis, Jose Osmar dos, Silva, Clailton Leopoldo da, Assunção, Frederico de Oliveira, Castioni, Julio Cezar Oliveira, Martins, Rafael, Xavier, Carlos Eduardo, Areias, Isac Antonio dos Santos, Ribeiro Junior, Ronny Francis, Lambert-Torres, Germano, Bonaldi, Erik Leandro, Silva, Luiz Eduardo Borges da, and Oliveira, Levy Ely Lacerda

Subjects
Signal processing, High-voltage HVCBs, Vibration analysis, Predictive Maintenance, Fault detection
Abstract

As high-voltage circuit breakers (HVCBs) are responsible for switching off the load in the event of anomalies, they suffer various wear and tear, both on their main contacts and on the other actuation mechanisms. Not only load maneuvers but also weather conditions can bring factors that contribute to deterioration and, consequently, contribute to failures of this component that is so important for energy supply. Both failures and maintenance shutdowns generate costs for substations, something that could be minimized if there was monitoring of the condition of the HVCBs. This paper shows a methodology to analyze the vibration signal of HVCB in order to identify and quantify contact failures. The proposed methodology is verified through an experimental setup. The results show that it is possible not only to identify the fault but also to assess its intensity using vibration analysis.

Published
2023

2. Studying Intelligent Techniques Acting in Large Power Transformer Monitoring

Author

Oliveira, Elvis Ricardo de, Araujo Junior, Vanias de, Cândido, José Faustino da Silva, Lambert-Torres, Germano, Silva, Luiz Eduardo Borges da, Bonaldi, Erik Leandro, Andrade, Gilberto Capistrano Cunha de, Oliveira, Levy Ely de Lacerda de, Moraes, Carlos Henrique Valério de, and Teixeira, Carlos Eduardo

Subjects
Artificial Intelligence, Data Analytics, Intelligent Techniques, Power Transformers
Abstract

The presented development is an intelligent diagnostic system for transformers that studied machine learning techniques to determine the operational status of these transformers. The study of these techniques is initiated by observing the quantities that define the operational behavior of large transformers, aiming to identify anomalies in their operation from data from sensors that equipment it in the functioning environment. This large power transformer has a theoretical service life of above 20 years and a low failure rate. Thus, obtaining failure values, which have their evolution monitored for large transformers, is almost nil. Therefore, a supervised machine training methodology to diagnose these cases is practically unfeasible. The study carried out with several traditional intelligent techniques can verify this. Several supervised methods (Closest Neighbor K-th Neighbor, Support Vector Machine, Radial Base Function, Decision Trees, Random Forest, Neural Network, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis) were studied.

Published
2023

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