http://repositorio.ufc.br/handle/riufc/74670 2026-04-14T10:01:21Z 2026-04-14T10:01:21Z Silva, Marcelo Estevão da Moura Filho, Joaquim Osterwald Frota Suni, Juan Carlos Peqqueña Amora, Márcio André Baima http://repositorio.ufc.br/handle/riufc/75936 2024-01-22T18:36:43Z 2022-01-01T00:00:00Z

Título: Classificação inteligente de faltas multiestágio em alimentadores primários de distribuição de energia elétrica Autor(es): Silva, Marcelo Estevão da; Moura Filho, Joaquim Osterwald Frota; Suni, Juan Carlos Peqqueña; Amora, Márcio André Baima Abstract: Distribution systems, due to their complex topologies and configurations, present the challenge of maintaining the reliability and continuity of the energy supply. In this sense, one of the main faults in the electrical network is the emergence of multi-stage faults, which represent 20% of fault occurrences. Aiming at the context of smart grids, and considering electricity meters that will be optimally allocated, this work proposes a methodology for classifying multistage faults in primary radial and overhead distribution feeders, based on decision trees (DA), whose Input parameters are the currents of the primary distribution feeder under study, measured only at the substation. The current oscillographs were obtained from simulations with the software ATLAB/SIMULINK and the signal processing method adopted was the RMS (Root Mean Square). Therefore, the obtained results represent an accurate classification, superior to 97%, indicating efficiency of the proposed method for the classification of such defects. Tipo: Artigo de Evento

2022-01-01T00:00:00Z Moura Filho, Joaquim Osterwald Frota Silva, Marcelo Estevão da Pinto, Valdilberto Pereira Amora, Márcio André Baima http://repositorio.ufc.br/handle/riufc/75933 2024-01-23T19:21:31Z 2022-01-01T00:00:00Z

Título: Modelagem da degradação em turbinas de aeronave sob condições reais de voo Autor(es): Moura Filho, Joaquim Osterwald Frota; Silva, Marcelo Estevão da; Pinto, Valdilberto Pereira; Amora, Márcio André Baima Abstract: The paper performs a modeling of the degradation curves in aircraft turbines under real flight conditions and also a comparison between machine learning techniques based on decision trees. The algorithms used are: Decision Trees (DT), Random Forest (RF) and Gradient Boosting (GB). Coefficient of determination, mean square error and root mean square error are employed as performance evaluation methods. The presented results show the best performance of RF and GB in estimating the values. The coefficients of determination of the algorithms reached average values higher than 0.98, thus showing the efficiency of the proposed models to be used in this application. Tipo: Artigo de Evento

2022-01-01T00:00:00Z Silva, Marcelo Estevão da Oliveira, Manoel E. N. Nunes, Felipe Becker M. Santos, Luiz Antonio G. Nascimento, Athur M. do Costa, Edênio Z. G. http://repositorio.ufc.br/handle/riufc/74756 2023-10-24T13:40:53Z 2021-01-01T00:00:00Z

Título: Classification of the Supply Voltage conditions of a Three-Phase Induction Motor with Machine Learning techniques Autor(es): Silva, Marcelo Estevão da; Oliveira, Manoel E. N.; Nunes, Felipe Becker M.; Santos, Luiz Antonio G.; Nascimento, Athur M. do; Costa, Edênio Z. G. Abstract: Three-Phase Induction Motors (TPIM) is a fundamental part, as they are the main responsible for carrying out the mechanical work process in the industry. It is estimated that they are responsible for consuming more than half of all energy destined for the industrial sector. Thus, any failure of operation in motors of this type is reflected in energy, economic and environmental losses. Among the most common failures is the unbalance of the supply voltages, which can cause total loss of the machine depending on the magnitude of the unbalance. This article addresses a comparative analysis between the Machine Learning K-Nearest Neighbors (KNN), Random Forest (RF), Suport Vector Machine (SVM), Principal Component Analysis (PCA) and Multilayer Perceptron Neural Network (MLP) techniques applied to the classification of unbalanced supply voltages of a three-phase induction motor. For this, a database was used with mechanical and electrical variables related to the balanced and unbalanced operation of the motor, divided into classes of different levels of unbalance according to the National Electrical Manufactores Association (NEMA). Tipo: Artigo de Evento

2021-01-01T00:00:00Z Moura Filho, Joaquim Osterwald Frota Silva, Marcelo Estevão da Amora, Márcio André Baima Pinto, Vandilberto Pereira Parente, Joan K. C. http://repositorio.ufc.br/handle/riufc/74754 2024-01-23T19:16:42Z 2021-01-01T00:00:00Z

Título: Diagnóstico de falhas em máquinas elétricas rotativas utilizando técnicas de ensemble learning Autor(es): Moura Filho, Joaquim Osterwald Frota; Silva, Marcelo Estevão da; Amora, Márcio André Baima; Pinto, Vandilberto Pereira; Parente, Joan K. C. Abstract: This article analyzes the problem of classifying faults in rotating electrical machines. For this, a comparison between Random Forest (RF) and Gradient Boosting (GB) ensemble learning techniques was performed in order to analyze the performance of these algorithms. The database used is composed of eight mechanical variables related to motor operation under failure and non-failure conditions. The feature extraction technique employed was Root Mean Square (RMS). Therefore, the simulations performed of the algorithms resulted in high success rates, with Gradient Boosting obtaining the best performance, with an accuracy higher than 99%, which reinforces the great applicability of these algorithms in problems of this nature. Tipo: Artigo de Evento

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