Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Rui E. Araújo received the electrical engineering graduation, M. Sc. and Ph. D. degrees from the Faculty of Engineering of the University of Porto, Portugal in 1987, 1992 and 2001, respectively. From 1987 to 1998, he was an Electrotechnical Engineer in Project Department, Adira Company, Porto, Portugal, and from 1988 to 1989, he was researcher with INESC, Porto, Portugal. Since 1989, he has been with the University of Porto, where he is an Assistant Professor with the Department of Electrotechnical and Computer Engineering at Faculty of Engineering. He is a Researcher in the Power Systems Unit of INESC PORTO. His research interests are focused on motion control and electric vehicles. Recently, his areas of interests include the design and control of grid-connected converters for micro-grids and electric vehicles.

Interest
Topics
Details

Details

  • Name

    Rui Esteves Araujo
  • Role

    Senior Researcher
  • Since

    01st April 2010
010
Publications

2024

Comparison between LightGBM and other ML algorithms in PV fault classification

Authors
Monteiro, P; Lino, J; Araújo, RE; Costa, L;

Publication
EAI Endorsed Trans. Energy Web

Abstract
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system. © 2024 P. Monteiro et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0. All Rights Reserved.

2024

Switched reluctance motor core loss estimation with a new method based on static finite elements

Authors
Melo, PS; Araújo, RE;

Publication
COGENT ENGINEERING

Abstract
Core loss estimation in switched reluctance motor is a complex task, due to non-linear phenomena and non-sinusoidal flux density waveforms. Several methods have been developed for estimating it (e.g. empirical, and physical-mathematic models), each one with merits and limitations. This paper proposes a new method for core losses estimation based on Finite Element Method Magnetics software. The main idea is using the machine phase-current harmonics as input for estimating core losses. In addition, a comparative study is carried out, where the proposed approach is faced up to a different one, based on Fourier decomposition of the flux density waveforms in the machine sections. In order to systematically analyze and compare the applied estimation cores loss techniques, a case study of a three-phase 6/4 SRM for different simulation scenarios is introduced. The outcomes of both methods are discussed and compared, where core loss convergence is found for limited speed and load ranges.

2024

Fuzzy Super-Twisting Sliding Mode Controller for Switched Reluctance Wind Power Generator in Low-Voltage DC Microgrid Applications

Authors
Touati, Z; Mahmoud, I; Araujo, RE; Khedher, A;

Publication
ENERGIES

Abstract
There is limited research focused on achieving optimal torque control performance of Switched Reluctance Generators (SRGs). The majority of existing studies tend to favor voltage or power control strategies. However, a significant drawback of SRGs is their susceptibility to high torque ripple. In power generation systems, torque ripple implicates fluctuations in the generated power of the generator. Moreover, high torque ripple can lead to mechanical vibrations and noise in the powertrain, impacting the overall system performance. In this paper, a Torque Sharing Function (TSF) with Indirect Instantaneous Torque Control (IITC) for SRG applied to Wind Energy Conversion Systems (WECS) is proposed to minimize torque ripple. The proposed method adjusts the shared reference torque function between the phases based on instantaneous torque, rather than the existing TSF methods formulated with a mathematical expression. Additionally, this paper introduces an innovative speed control scheme for SRG drive using a Fuzzy Super-Twisting Sliding Mode Command (FSTSMC) method. Notably robust against parameter uncertainties and payload disturbances, the proposed scheme ensures finite-time convergence even in the presence of external disturbances, while effectively reducing chattering. To assess the effectiveness of the proposed methods, comprehensive comparisons are made with traditional control techniques, including Proportional-Integral (PI), Integral Sliding Mode Control (ISMC), and Super-Twisting Sliding Mode Control (STSMC). The simulation results, obtained using MATLAB (R)/SIMULINK (R) under various speeds and mechanical torque conditions, demonstrate the superior performance and robustness of the proposed approaches. This study presents a thorough experimental analysis of a 250 W four-phase 8/6 SRG. The generator was connected to a DC resistive load, and the analysis focuses on assessing its performance and operational characteristics across different rotational speeds. The primary objective is to validate and confirm the efficacy of the SRG under varying conditions.

2024

Guest Editorial Introduction to the Special Section on Next Generation Zero-Emission Vehicles

Authors
de Castro, R; Moura, S; Esteves, RE; Corzine, K;

Publication
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

Abstract
This special section features extended versions of papers originally published in the 2022 IEEE Vehicle Power and Propulsion Conference (VPPC22), hosted by the University of California, Merced, USA. This was the first time that the VPPC took place in California, USA. It was a timely visit. California recently announced that only zero-emission vehicles (ZEVs) will be allowed to be sold in the state by 2035. Other states and countries will surely follow. The VPPC, as one of the pioneer forums dedicated to electric mobility, is in a privileged position to create and disseminate knowledge that will help our communities transition toward sustainable transportation, improving air quality and reducing greenhouse emissions.

2023

Model-Free Finite-Set Predictive Current Control With Optimal Cycle Time for a Switched Reluctance Motor

Authors
Pereira, M; Araujo, RE;

Publication
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Abstract
Traditional use of predictive control techniques require the knowledge of the systems model to control and the use of constant cycle-time. In the case of a switched reluctance motor its model is highly nonlinear and time-varying with current magnitude and rotor position. The use of look-up tables has been one solution, but requires a complete knowledge of the motor and mismatches from the original model used in the design can happen due temperature variation or changes in operating regimes. To address these issues as well as to increase the tracking performance of current control, a model-free predictive algorithm is developed by updating the next cycle time of the next time step of the predictive control. A new parameter estimation method is proposed that identifies the parameters of the switched reluctance model with low computational burden. Based on knowledge of the parameters at real time, not only the ideal voltage vector is applied at each cycle but the ideal time that each cycle must have is also calculated. As result, the advanced current controller requires almost no knowledge of the motor in use. The performance of the proposed schemes is validated through simulation and by a prototype experimental setup. Experimental data shows a decreasing in prediction error around 78 per cent, when comparing to the predefined model controller.

Supervised
thesis

2022

An intelligent approach to fault tolerance in cyber physical systems

Author
Luis Fernando Piardi

Institution
UNL-FCTNOVA

2022

Transporte Robotizado de Bagagens em Aeroportos

Author
PEDRO MATIAS LOPES LEMOS

Institution
IPP-ISEP

2022

Impact of demand response and electrical energy storage on multi-energy systems management

Author
Morteza Vahid Ghavidel

Institution
UP-FEUP

2021

CMOS Design for Indoor Photovoltaic Harvesting

Author
Ana Luísa Cardoso Macedo

Institution
UP-FEUP

2021

Blind or partially sighted visitors in museums: enhancing the visitors’ experience through assistive technologies

Author
Roberto Ivo Fernandes Vaz

Institution
UP-FEUP