Vitor Hugo Ferreira

Scopus Publications

Long term wind energy forecasting using machine learning techniques
Marcos V.M. Siqueira, Vitor H. Ferreira, Angelo C. Colombini
Global Energy Interconnection, 2025
MOEPSO-based Reactive Power Control in Distribution Systems with DG Sources
Rene Cruz Freire, Angelo Cesar Colombini, Vitor Hugo Ferreira
2025 International Symposium on Internet of Things Siot 2025, 2025
A New Clustering Approach Applied to Oscillographs
Andre da Costa Pinho, Vitor Hugo Ferreira, Alexandre Brito Gantos Borges, Marcio Zamboti Fortes
Proceedings of the 2025 IEEE Pes Innovative Smart Grid Technologies Conference Latin America Isgt La 2025, 2025
Information Theoretic Learning Applied to Daily Streamflow Forecast and Its Impact on the Brazilian Hourly Energy Spot Prices
Elson Antonio Nunes, Vitor Hugo Ferreira, André da Costa Pinho
Journal of Control Automation and Electrical Systems, 2024
Assessment of Harmonic Power Losses in LED-Based Public Lighting Systems Using IEEE 1459:2010 Method
Adriano Pinheiros Fragoso, Eduardo Lourenço de Sousa, Marcio Zamboti Fortes, Estefânia Teixeira Perro, Vitor Hugo Ferreira
Lascas 2024 15th IEEE Latin American Symposium on Circuits and Systems Proceedings, 2024
Neglected frequencies analysis on switching operations in extra-high voltage electrical power substations
Felipe H B Bittar, Andrés P L Barbero, Vitor Hugo Ferreira, Alexandre B dos Santos, Dickson S de Souza, et al.
Electric Power Systems Research, 2023
Time series causal relationships discovery through feature importance and ensemble models
Manuel Castro, Pedro Ribeiro Mendes Júnior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Gonçalves, et al.
Scientific Reports, 2023
Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance , which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery , i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target’s value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.
A Machine Learning Framework for Data Driven Platform Electrical Systems Design and Simulation
V. H. Ferreira, D. C. Araújo, G. R. Mafra, M. Z. Fortes, A. C. Pinho, et al.
Offshore Technology Conference Brasil Otcb 2023, 2023
FPSO platforms have traditionally employed conservative designs for their electrical systems, resulting in lower load conditions than originally anticipated during the design phase. This work proposes a machine learning framework for data driven FPSO electrical systems design and simulation, with several machine learning and data analysis routines being implemented. The user experience design was done with the objective of the tool being easy to understand and use by engineering teams responsible for management level decisions. To achieve this an iterative development process was employed using target users’ feedback, with the final automatic regression functionality achieving an average mean absolute error of 0.87% per platform on validation data. Some features, such as the clustering functionality were specifically requested during the development, while others, such as the automated data preprocessing pipeline, were designed to minimize the need for user interference while maintaining a high-quality dataset for the user to work with. Developed in the background of a partnership between a research team at UFF and Petrobras, the framework was found appropriate and is currently at an early adoption phase by relevant teams at Petrobras.
A New Hybrid Data-Driven and Model-Based Methodology for Improved Short-Term Production Forecasting
Vitor Hugo de Sousa Ferreira, Manuel Castro, Renato Moura, Rafael de Oliveira Werneck, Marcelo Ferreira Zampieri, et al.
Proceedings of the Annual Offshore Technology Conference, 2023
Model-based (MB) solutions are widely used in reservoir management and production forecasting throughout the life-cycle of oil fields. However, such approaches are not often used for short-term (up to six months) forecasting due to the immediate-term productivity missmatch and the large number of models required to honor uncertainties. Recently developed data-driven (DD) techniques have shown promising performance in immediate term forecasting (from days to months) while losing performance as the timeframe increases. This work, proposes and investigates a hybrid methodology (HM) that combines MB and DD techniques focusing on improving the short-term production forecast. A common practice in reservoir management to understand the impact of uncertainties, is to build an ensemble of simulation model scenarios to assess the impact of these uncertainties on production forecasts. The proposed HM relies on the DD-assisted selection of a subset of models from the set of assimilated (posterior) models. Specifically, the pool of MB models is ranked based on their similarities to the DD production forecasts in the immediate term (e.g., one month), followed by the selection of the top models. The selected MB models are then used in the short-term forecasting task. In a case study for an offshore pre-salt reservoir benchmark, the proposed HM is compared to two baselines: one purely DD and another fully MB. The case study considered two forecasting conditions: human intervention-free with restrictions (HIF-R), with no intervention in the controls except to follow physical restrictions, and with human interventions (WHI), following optimization rules. Our results showed that the HM significantly outperformed the MB baseline, regardless of forecasting condition (HIF-R and WHI) or variables (pressure and oil/water/gas rates) for all evaluation metrics (time series similarity and rank-based) and top-selected models tested. The hybrid approach also helped improve the well productivity uncertainty that emerged from the data assimilation. Such results indicate that the performance of MB short-term forecasts can be enhanced when assisted by DD techniques, such as in our proposed HM. Comparing these two approaches, the best forecasts were split between the HM and the DD baseline. In the partially idealized HIF-R conditions, the DD baseline was best when the forecast trend was steady. However, the HM was superior for the more complex production behaviors. In the more realistic WHI conditions, the HM outperformed the DD baseline in almost every aspect tested given the inability of the chosen DD technique to leverage known interventions. This work is the first effort to improve MB short-term production forecasts, using production data, with a machine learning technique through a proposed HM. The proposed DD-assisted selection of models proved successful in a benchmark case study, which means it is promising for application in other fields and for further development.
Monitoring and Diagnostic System for Dry-Type Transformers Using Machine Learning Techniques
F. G. R. Martins, Y. Lopes, B. W. França, V. H. Ferreira, G. G. Sotelo, et al.
Offshore Technology Conference Brasil Otcb 2023, 2023
Power transformers are recognized as high-value assets in substation design, but their susceptibility to various failure modes poses a significant risk of damage and power supply disruptions. Consequently, extensive research has been conducted to develop diagnostic techniques and monitoring methodologies for these devices. This project aims to develop a comprehensive solution comprising hardware and software components for the online diagnosis of dry-type transformers, primarily focusing on the detection of Inter-Turn Short Circuits (ITSC) in conjunction with Partial Discharge (PD) signatures. Dry-type transformers utilize ambient air as both a cooling and insulating medium. Among its advantages, the most relevant for the oil and gas industry are the lower maintenance costs and the absence of flammable oil, ensuring lower fire risks, which is a critical factor in facilities. As offshore electrical plants grow in complexity, there is an increasing demand for dry-type transformers with higher power ratings. Effectively monitoring the operational condition of such transformers serves as a strategic tool to enhance the reliability, robustness and safety of the electrical system, while potentially reducing overall maintenance expenditures. Such transformers offer notable advantages in terms of safety and reliability [1]. However, they come with higher costs and have lower power and voltage limits. Similar to any other equipment, transformers experience aging as a natural consequence of their operation. Its most significant consequence is the gradual degradation of insulation due to thermal effects and mechanical stresses resulting from electromagnetic interactions between windings turns. Additionally, transformers are susceptible to short-circuits, which induce intense electromechanical stresses in the windings, as well as overvoltages stemming from maneuvers such as line energization or the presence of inductive or capacitive loads. These factors elevate the dielectric stress on insulating materials and connections, potentially surpassing their design limits.
Two-phase optimization approach for maintenance workforce planning in power distribution utilities
Vitor Hugo Ferreira, Paulo de M. Oliveira Filho, Eduardo Vieira Queiroga, João M.P. Silva, Eduardo Uchoa Barboza, et al.
Electric Power Systems Research, 2022
Improving LED certification using multi-criteria decision-making method methods
Lorenna B Oliveira, Júlia F Araujo, Marcio Z Fortes, Níssia CR Bergiante, Vitor H Ferreira, et al.
Lighting Research and Technology, 2022
Big data analytics for spatio-temporal service orders demand forecasting in electric distribution utilities
Vitor Hugo Ferreira, Rubens Lucian da Silva Correa, Angelo Cesar Colombini, Márcio Zamboti Fortes, Flávio Luis de Mello, et al.
Energies, 2021
A new clustering approach for automatic oscillographic records segmentation
Vitor Hugo Ferreira, André da Costa Pinho, Dickson Silva de Souza, Bárbara Siqueira Rodrigues
Energies, 2021
Maintenance Planning of Electric Distribution Systems—A Review
Caroline Trentini, Wanessa de Oliveira Guedes, Leonardo Willer de Oliveira, Bruno Henriques Dias, Vitor Hugo Ferreira
Journal of Control Automation and Electrical Systems, 2021
Optimal distributed generation allocation in unbalanced radial distribution networks via empirical discrete metaheuristic and steepest descent method
Francisco Carlos Rodrigues Coelho, Ivo Chaves da Silva Junior, Bruno Henriques Dias, Wesley Peres, Vitor Hugo Ferreira, et al.
Electrical Engineering, 2021
Evaluation of the Maturity in the Management system of Maintenance of Electricity Distribution Networks – A Case Study
Lorenna Baptista de Oliveira, Nissia Carvalho Rosa Bergiante, Marcio Zamboti Fortes, Vitor Hugo Ferreira, Bernardo Salgado Moreira, et al.
Journal of Engineering Science and Technology Review, 2021
Economic evaluation of a CHP biomass plant using stochastic dual dynamic programming
Caio Monteiro Leocadio, Carlos Salvador Gomide Richa, Marcio Zamboti Fortes, Vitor Hugo Ferreira, Bruno Henriques Dias
Electrical Engineering, 2020
Building a Low-Cost Weather Station
Tayna Ferreira Santos, Vitor Hugo Ferreira
IEEE Potentials, 2020
Monitoring technical losses to improve non-technical losses estimation and detection in LV distribution systems
H.O. Henriques, R.L.S. Corrêa, M.Z. Fortes, B.S.M.C. Borba, V.H. Ferreira
Measurement Journal of the International Measurement Confederation, 2020
PROPOSAL OF COMPUTATIONAL TOOL DO SUPPORT MAINTENANCE DECISION FOR ELECTRIC POWER GRIDS
Marcelo Carlos Afonso Carvalho, Matheus Coutinho Cunha, Igor Assumpção Melo, Márcio Zamboti Fortes, Angelo Cesar Colombini, et al.
Journal of Engineering and Technology for Industrial Applications, 2020
Probabilistic transmission line fault diagnosis using autonomous neural models
Vitor H. Ferreira, Rainer Zanghi, Márcio Z. Fortes, Sergio Gomes, Alexandre P. Alves da Silva
Electric Power Systems Research, 2020
Coupled electromagnetic-thermal simulation of a power transformer by 3d fem
Henrique Oliveira Henriques, Carlos Eduardo Vizeu, Paulo Cesar Souza, Mauricio Caldora Costa, Guilherme Gonçalves Sotelo, et al.
Acta Polytechnica, 2020
Transformer Fleet Optimal Maintenance with Risk Considerations
Alexandre P. Alves da Silva, Christian Ducharme, Vitor H. Ferreira
Electric Power Components and Systems, 2019
Artificial Neural Network-Based Protection Scheme of Active Distribution Systems
Fábio Casale Padovani Neto, Tatiana Mariano Lessa Assis, Vitor Hugo Ferreira
2019 IEEE Pes Conference on Innovative Smart Grid Technologies Isgt Latin America 2019, 2019
Machine Learning Applied to Shutdown Risk Estimation in Transmission System Maintenance Interventions
T.F. Santos, V.H. Ferreira, M.Z. Fortes, V.A. Gonzales, A.S.S. Rosa
2019 IEEE Pes Conference on Innovative Smart Grid Technologies Isgt Latin America 2019, 2019
A Comparative Analysis between Particle Swarm Optimization and Mathematical Programming for Optimal Location of Maintenance Facilities
Paulo M. O. Filho, Bruno S. M. C. Borba, Vitor H. Ferreira, Rafael V. Tavares, Eduardo Queiroga, et al.
2019 IEEE Pes Conference on Innovative Smart Grid Technologies Isgt Latin America 2019, 2019
A review on optimization methods for workforce planning in electrical distribution utilities
Bruno S.M.C. Borba, Marcio Z. Fortes, Leonardo A. Bitencourt, Vitor H. Ferreira, Renan S. Maciel, et al.
Computers and Industrial Engineering, 2019
Evaluation of the impact of LED and compact fluorescent lamps on the PLC transmission with X-10 technology
A. S. Delfino, R. P. Brandao, T. S. Bumpus, M. Z. Fortes, H. O. Henriques, et al.
2019 IEEE Milan Powertech Powertech 2019, 2019
A rain-streamflow model for prediction of limnimetric behavior of reservoirs using artificial neural networks
Guilherme Flores, Vitor Hugo Ferreira
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Voltage control in microgrids with minimum adjustment in distributed generation units
Tayna Ferreira Santos, Vitor Hugo Ferreira
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Artificial neural network for probabilistic forecasting of the output power of photovoltaic systems
Rodrigo Pizetti Martins, Vitor Hugo Ferreira, Thales Terrola e Lopes
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Variable step-size algorithm for transient stability programs
Paulo P. Machado, Sergio Gomes, Vitor Hugo Ferreira
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Design of a real-time, low-cost monitoring system for hybrid solar-wind power generation system
Wagner de Anchieta Marques, Vitor Hugo Ferreira, Guilherme Gonçalves Sotelo
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Prediction of electric power systems influences on pipeline systems using artificial neural networks
Luciano Coelho, Vitor Hugo Ferreira, Guilherme Sotelo, Antonio Lima
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Islanding detection in distributed generation using supervised learning techniques
Bruno Martins de Biaz, Thales Terrola e Lopes, Márcio Zamboti Fortes, Vitor Hugo Ferreira
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Topologies classification employed in LED lamps through supervised learning
Ana C. T. Carvalho, Juan F. H. Guardiola, Luiz E. Barrientos, Marcio Z. Fortes, Vitor H. Ferreira, et al.
Sbse 2018 7th Brazilian Electrical Systems Symposium, 2018
Evaluation of Carbon Fiber Cables in Transmission Lines
Tarcisio Silva Lessa, Daniel Souto Lopes, Paulo Sérgio Fonseca Antunes, Sergio M. D. Rocha Filho, Marcio Zamboti Fortes, et al.
Electric Power Components and Systems, 2018
Multiobjective swarm optimization for operation planning of electric power systems
Rene Cruz Freire, Vitor Hugo Ferreira, Renan Silva Maciel
Swarm Intelligence Volume 3 Applications, 2018
Islanding Detection in Distributed Generation using Unsupervised Learning Techniques
Bruno Martins Biaz, Vitor Hugo Ferreira, Marcio Zamboti Fortes, Thales Terrola Lopes, Gilson Brito Alves Lima
IEEE Latin America Transactions, 2018
Harmonic analysis of lighting technology application - case study in distribution network: smart city Buzios
Ana Paula, Adriano Fragoso, Márcio Fortes, Vitor Ferreira, Antonio Pereira
Cired Open Access Proceedings Journal, 2017
Technical and financial assessments for comparison of measurement technologies in the Smart City Buzios Project
Vitor Ferreira, Marcio Fortes, Bruno Borba, Wagner Marques, Renan Maciel, et al.
Cired Open Access Proceedings Journal, 2017
DG impact evaluation on LV distribution grids using AMI data: a Brazilian case study
Renan Maciel, Thomas Campello, Milon Silva, Bruno Borba, Leticia Fritz, et al.
Cired Open Access Proceedings Journal, 2017
Autonomous Bayesian neural models for fault diagnosis in transmission lines
V. H. Ferreira, D. S. Lopes, M. Z. Fortes, S. Gomes, R. Zanghi
2017 IEEE Manchester Powertech Powertech 2017, 2017
Optimal EV charging and discharging control considering dynamic pricing
Leonardo De A. Bitencourt, Bruno S.M.C. Borba, Renan S. Maciel, Marcio Z. Fortes, Vitor H. Ferreira
2017 IEEE Manchester Powertech Powertech 2017, 2017
A Nonparametric Approach for River Flow Forecasting Based on Autonomous Neural Network Models
Vitor Hugo Ferreira, Alexandre P. Alves da Silva
Advances in Electric Power and Energy Systems Load and Price Forecasting, 2017
Short-term load forecasting
P. Alexandre, Alves da Silva, Vitor H. Ferreira
Electric Power Systems Advanced Forecasting Techniques and Optimal Generation Scheduling, 2017
Application of neural networks techniques and atmospheric modeling to elaborate inflow forecasts in the Rio Grande Basin (MG), Brazil
Talita Lopes Dias, Marcio Cataldi, Vitor Hugo Ferreira
Engenharia Sanitaria E Ambiental, 2017
New Trends in Power Quality Event Analysis: Novelty Detection and Unsupervised Classification
André Eugenio Lazzaretti, Vitor Hugo Ferreira, Hugo Vieira Neto
Journal of Control Automation and Electrical Systems, 2016
Electronic metering in Brazil: A qualitative analysis for its evolution
R. A. Oliveira, G. F. Flores, B. M. Biaz, V. H. Ferreira, N. C. Fernandes, et al.
Proceedings of the 2nd International Conference on Intelligent Green Building and Smart Grid Igbsg 2016, 2016
Distribution network automation using power switches and reclosers
Universidade Federal Fluminense, Niterói, RJ, Brasil., Carolina Pinchemel Teixeira, Diego Macedo Pedreira Lameirão, Universidade Federal Fluminense, Niterói, RJ, Brasil., Moises Ávila Oliveira, et al.
Ciencia Y Engenharia Science and Engineering Journal, 2016
A survey on intelligent system application to fault diagnosis in electric power system transmission lines
V.H. Ferreira, R. Zanghi, M.Z. Fortes, G.G. Sotelo, R.B.M. Silva, et al.
Electric Power Systems Research, 2016
Novelty detection and multi-class classification in power distribution voltage waveforms
André Eugênio Lazzaretti, David Martinus Johannes Tax, Hugo Vieira Neto, Vitor Hugo Ferreira
Expert Systems with Applications, 2016
Photovoltaic generation allocation on a radial distribution feeder - Case study
C. P. Teixeira, M. A. Oliveira, M. R. Santos, M. Z. Fortes, V. H. Ferreira, et al.
Chilecon 2015 2015 IEEE Chilean Conference on Electrical Electronics Engineering Information and Communication Technologies Proceedings of IEEE Chilecon 2015, 2016
Some considerations about energy quality in systems deployed at Smart City Búzios
M. Z. Fortes, V. H. Ferreira, A. M. E. Pereira, B B S. Penna, I. S. Machado, et al.
Chilecon 2015 2015 IEEE Chilean Conference on Electrical Electronics Engineering Information and Communication Technologies Proceedings of IEEE Chilecon 2015, 2016
Electrical power systems engineering undergraduate courses in Brazil: Academic and market perspectives
Vitor H Ferreira, Marcio Z Fortes, Marcus VCM Bouças
International Journal of Electrical Engineering and Education, 2016
Fault Diagnosis in Transmission Lines: Trends and Main Research Areas
Marcio Zamboti Fortes, Vitor Hugo Ferreira, Rainer Zanghi
IEEE Latin America Transactions, 2015
Harmonic analysis of distributed generation in Smart City Búzios project
M. Z. Fortes, V. H. Ferreira, I. S. Machado, W. C. Fernandes
2015 IEEE Workshop on Power Electronics and Power Quality Applications Pepqa 2015 Proceedings, 2015
An accurate approach for automatic segmentation of power distribution voltage waveforms
Andre E. Lazzaretti, Hugo Vieira Neto, Vitor H. Ferreira
IEEE Transactions on Power Delivery, 2015
Deployment of smart metering in the Búzios City
M.Z. Fortes, V.H. Ferreira, G.G. Sotelo, AS. Cabral, W. F. Correia, et al.
2014 IEEE Pes Transmission and Distribution Conference and Exposition Pes T and D La 2014 Conference Proceedings, 2014
A new approach for event classification and novelty detection in power distribution networks
Andre E. Lazzaretti, Vitor H. Ferreira, Hugo Vieira Neto, Luiz F. R. B. Toledo, Cleverson L. S. Pinto
IEEE Power and Energy Society General Meeting, 2013
Autonomous neural models for the classification of events in power distribution networks
André E. Lazzaretti, Vitor H. Ferreira, Hugo Vieira Neto, Rodrigo J. Riella, Julio S. Omori
Journal of Control Automation and Electrical Systems, 2013
The induction motor parameter estimation using genetic algorithm
Marcio Zamboti Fortes, Vitor Hugo Ferreira, Alex Palma Francisco Coelho
IEEE Latin America Transactions, 2013
A new approach for event detection in smart distribution oscillograph recorders
Andre E. Lazzaretti, Vitor H. Ferreira, Hugo Vieira Neto, Luiz F. R. B. Toledo, Cleverson L. S. Pinto
2013 IEEE Pes Conference on Innovative Smart Grid Technologies Isgt La 2013, 2013
Autonomous neural models applied to medium-term water inflow forecasting
V.H. Ferreira, C.M. Leocadio
2011 16th International Conference on Intelligent System Applications to Power Systems Isap 2011, 2011
Natural optimization applied to medium-term hydrothermal coordination
V. H. Ferreira, G. H. C. Silva
2011 16th International Conference on Intelligent System Applications to Power Systems Isap 2011, 2011
Chaos theory applied to input space representation of autonomous neural network-based short-term load forecasting models
Vitor Hugo Ferreira, Alexandre Pinto Alves da Silva
Controle Y Automacao, 2011
Fully automatic Bayesian neural forecaster - NN GC1
Vitor Hugo Ferreira, Alexandre P. Alves da Silva
Proceedings of the International Joint Conference on Neural Networks, 2010
Automatic kernel based models for short term load forecasting
Vitor Hugo Ferreira, Alexandre Pinto Alves da Silva
2009 15th International Conference on Intelligent System Applications to Power Systems Isap 09, 2009
Demand forecasting for control of the use of transmission system for electric distribution utilities vitor
V.H. Ferreira, A.R. Aoki, S.M. de Rocco
2009 15th International Conference on Intelligent System Applications to Power Systems Isap 09, 2009
Classification of events in distribution networks using autonomous neural models
A.E. Lazzaretti, V.H. Ferreira, H.V. Neto, R.J. Riella, J. Omori
2009 15th International Conference on Intelligent System Applications to Power Systems Isap 09, 2009
Input space to neural network based load forecasters
Alexandre P. Alves da Silva, Vitor H. Ferreira, Roberto M.G. Velasquez
International Journal of Forecasting, 2008
Toward estimating autonomous neural network-based electric load forecasters
Vitor Hugo Ferreira, Alexandre P. Alves da Silva
IEEE Transactions on Power Systems, 2007
Complexity control of neural models for load forecasting
V.H. Ferreira, A.P. Alves da Silva
Proceedings of the 13th International Conference on Intelligent Systems Application to Power Systems Isap 05, 2005

Vitor Hugo Ferreira

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