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About

Dr. Leonel Carvalho was born in Espinho, Portugal, in 1985. He received his B.Sc., M.Sc., and Ph.D. degrees in Electrical Engineering from the Faculty of Engineering of the University of Porto (FEUP), Portugal, in 2006, 2008, and 2013, respectively. Currently, he is a senior researcher at INESC TEC. In 2011, he was a Visiting Researcher at the Institute of Electric Systems and Energy of the Federal University of Itajubá (UNIFEI), Minas Gerais, Brazil, where he was engaged in research activities related with the use of the Cross-Entropy Method for improving the Reliability Assessment of large-scale power systems. In 2014, he was the winner of the IEEE International Competition on the Application of Modern Heuristic Optimization Algorithms for Solving Optimal Power Flow Problems organized by the IEEE PES Working Group on Modern Heuristic Optimization, with the algorithm entitled “DEEPSO as a successful blend of evolutionary and swarm search strategies in the OPF challenge”. In 2015, he held the Auxiliary Professor position at the Universidade Lusíada of Vila Nova de Famalicão where he was responsible of several courses of the Licenciatura degree in Eletronic and Computer Engineering. Dr. Leonel has co-supervised several M.Sc. theses, one of which was granted the first place in 2015 edition of the prestigious REN Prize, which is an award aiming at distinguishing the best M.Sc. theses completed in Portuguese higher education institutions in the fields of Engineering, Economics, Mathematics, Physics, Chemistry, Information Systems and Computer Science. As a researcher in INESC TEC, he has been involved in several national and international R&D projects amongst which is worth highlighting the RESERVE project with the Portuguese TSO, the ARGUS project with the Argonne National Laboratory in the USA, the FP7 projects MERGE, STABALID, evolvDSO, and iTESLA and the H2020 project SENSIBLE. He has authored and co-authored several papers in peer-reviewed journals as well as in international conferences. His current research interests include power system Reliability Assessment and the application of Computational Intelligence algorithms to power system optimization problems.

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Details

Details

  • Name

    Leonel Magalhães Carvalho
  • Cluster

    Power and Energy
  • Role

    Area Manager
  • Since

    18th February 2008
027
Publications

2021

Multi-objective identification of critical distribution network assets in large interruption datasets

Authors
Marcelino, CG; Torres, V; Carvalho, L; Matos, M; Miranda, V;

Publication
International Journal of Electrical Power and Energy Systems

Abstract
Performance indicators, such as the SAIFI and the SAIDI, are commonly used by regulatory agencies to evaluate the performance of distribution companies (DisCos). Based on such indicators, it is common practice to apply penalties or grant rewards if the indicators are greater to or less than a given threshold. This work proposes a new multi-objective optimization model for pinpointing the critical assets involved in outage events based on past performance indicators, such as the SAIDI and the System Average Interruption Duration Exceeding Threshold (SAIDET) indexes. Our approach allows to retrieve the minimal set of assets in large historical interruption datasets that most contribute to past performance indicators. A case study using a real interruption dataset between the years 2011–2104 from a Brazilian DisCo revealed that the optimal inspection plan according to the decision maker preferences consist of 332 equipment out of a total of 5873. This subset of equipment, which contribute 61.90% and 55.76% to the observed SAIFI and SAIDET indexes in that period, can assist managerial decisions for preventive maintenance actions by prioritizing technical inspections to assets deemed as critical. © 2021

2021

Optimal Power Flow Solution for Distribution Networks using Quadratically Constrained Programming and McCormick Relaxation Technique

Authors
Javadi, MS; Gouveia, CS; Carvalho, LM; Silva, R;

Publication
2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Abstract

2021

An unsupervised approach for fault diagnosis of power transformers

Authors
Dias, L; Ribeiro, M; Leitao, A; Guimaraes, L; Carvalho, L; Matos, MA; Bessa, RJ;

Publication
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

2020

Aggregated dynamic model of active distribution networks for large voltage disturbances

Authors
Fulgencio, N; Moreira, C; Carvalho, L; Lopes, JP;

Publication
Electric Power Systems Research

Abstract

2020

A Hierarchical Optimization Strategy for Energy Scheduling and Volt/var Control in Autonomous Clusters of Microgrids

Authors
Castro, MV; Moreira, C; Carvalho, LM;

Publication
IET Renewable Power Generation

Abstract

Supervised
thesis

2021

CONGRATS – Convolutional Networks in GPU-based Reliability Assessment of Transmission Systems

Author
Rodrigo Gonçalves de Morais

Institution
UP-FEUP

2021

Integrated Renewable Storage Systems Under Artificial Intelligence Decision Models

Author
Tiago João Amorim Abreu

Institution
UP-FEUP

2019

MOCAPIRA - Monte Carlo parallel implementation for reliability assessment

Author
Inês Maria Afonso Trigo de Freitas Alves

Institution
UP-FEUP

2015

Impacto do Erro da Previsão Eólica nas Necessidades a Longo-Prazo de Reserva Operacional

Author
João Teixeira

Institution
UP-FEUP