Zobrazeno 1 - 10 of 149 pro vyhledávání: '"A.P. Alves da Silva"'
1
Transformer Fleet Optimal Maintenance with Risk Considerations
Autor: A.P. Alves da Silva, Christian Ducharme, Vitor Hugo Ferreira
Publikováno v: Electric Power Components and Systems. 47:1551-1561
This paper aims at proposing an optimization model for high voltage transformers’ maintenance scheduling to minimize operational cost and risk. The focus of the paper is on the transformer fleet ma...
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8fbdc07aeb026dff276390737b17f8a4
https://doi.org/10.1080/15325008.2019.1661546
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2
Probabilistic transmission line fault diagnosis using autonomous neural models
Autor: Marcio Zamboti Fortes, S. Gomes, Vitor Hugo Ferreira, A.P. Alves da Silva, Rainer Zanghi
Publikováno v: Electric Power Systems Research. 185:106360
Fault diagnosis is a critical task when a severe disturbance is caused by insulation failure on a transmission line. Fault diagnosis should be concluded as soon as possible to avoid further financial and social costs because of load interruptions. In
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8de3ce13097f1cc979f2658d4df7b94d
https://doi.org/10.1016/j.epsr.2020.106360
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3
A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models
Autor: A.P. Alves da Silva, Vitor Hugo Ferreira
Publikováno v: Advances in Electric Power and Energy Systems
This chapter presents an inductive learning procedure that combines several techniques to generate a fully data‐driven forecasting model. It considers a forecasting method based on appropriate techniques for controlling Artificial neural networks (
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c7983c14cd14cb9c2b9883ac37e0c1fa
https://doi.org/10.1002/9781119260295.ch8
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4
Akademický článek
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9
Neural Network Based Short-Term Electric Load Forecasting with Confidence Intervals
Autor: A.P. Alves da Silva, L.S. Moulin
Publikováno v: Anais do 4. Congresso Brasileiro de Redes Neurais.
Through traditional statistical models, like ARMA and Multilinear Regression, confidence intervals can be computed for the short-term electric load forecasting, assuming that the forecast errors follow a normal probability distribution. In this paper
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::da5868c55d6f3eeffccabae4afba2756
https://doi.org/10.21528/cbrn1999-002
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