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Sobre

Sobre

Vladimiro Miranda was born in Porto, Portugal. He graduated in Electrical Engineering in 1977 and received the Ph.D. degree in Electrical Engineering from FEUP, the Faculty of Engineering of the University of Porto, Portugal, in 1982. He joined FEUP in 1981 and joined INESC in 1985, a top R&D institute in Portugal where he came to be coordinator of the area of Power Systems during the 90’s.

He was a member of the Board of Directors of INESC TEC, Portugal - an R&D private non-profit organization recognized by the Ministry of Science and with the University of Porto as the main associate - for 18 years until June 2018, and holds presently the following responsibilities:

  • Full Professor (Professor Catedrático) at FEUP, University of Porto, Portugal
  • Director-President of INESC P&D Brasil, an R&D private non-profit organization with headquarters in São Paulo, Brazil.
  • Associate Director at INESC TEC, International Affairs.
  • Member of the Doctoral Council of UTAD (University of Trás os Montes e Alto Douro), Portugal.

He is International Scientific Advisor for:

  • IRESEN, Agency associated to the Ministry of Energy, Morocco
  • Hong Kong Polytechnic University, China
  • Instituto de Investigación Tecnológica (Madrid), Spain
  • Instituto de Energía Eléctrica (San Juan), Argentina
  • Laboratory for Biologic and Chemical Defense of the Portuguese Army, Portugal

He was invited as Honorary Professor of the Novi Sad University, Serbia, and was President of INESC Macau, China. In 1996 and 1997 he stayed as Visiting Full Professor in the University of Macau, China. In 2005 he was Visiting Professor at the Federal University of Pará (UFPA), Brazil, and in 2015-2018 he was visiting researcher at the Federal University of Santa Catarina (UFSC), Brazil.

Prof. Miranda has been serving in the Administration Board of spin-off companies created within the INESC system. He has also served as research project evaluator for the governmental science organizations of Portugal, Norway, Croatia, South Africa, Chile, Brazil and Argentina. For the Government of this latter country, he acted as external auditor in the process of evaluation of research institutions.

He has supervised, co-supervised or cooperated in the supervision of a large number of PhD and MSc theses e power systems in several countries and universities such as in Portugal, Brazil, Argentina, Bosnia, China, Ecuador, Norway or Sweden. 

He has been responsible for many research projects at international level, in the European Union, United States and Brazil, and has authored or co-authored over 200 publications, especially in areas related with Power Systems and the application of Computational Intelligence to Power Systems.

He has been a member (at times the chairman) of the organizing or scientific committees of several important conferences in his areas of expertise such as PMAPS, ISAP, IEEE Power Tech, etc.

Prof. Miranda is an IEEE Fellow.

He is the recipient in 2013 of the IEEE Power Engineering Society Ramakumar Family Renewable Energy Excellence Award.

He is a member of the IEEE Distinguished Lecturer Program.

Curriculum LATTES:  http://lattes.cnpq.br/5824178098755298

Scopus Author ID: 35581693000  -  AuthenticusID: R-000-HPD

(end)

Detalhes

Detalhes

  • Nome

    Vladimiro Miranda
  • Cluster

    Energia
  • Cargo

    Diretor Associado
  • Desde

    01 março 1985
007
Publicações

2022

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

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

Publicação
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & 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

2022

Bayesian Inference Approach for Information Fusion in Distribution System State Estimation

Autores
Massignan, JAD; London, JBA; Bessani, M; Maciel, CD; Fannucchi, RZ; Miranda, V;

Publicação
IEEE TRANSACTIONS ON SMART GRID

Abstract
This paper presents a three-phase Distribution System State Estimator (DSSE) based on a Bayesian inference approach to manage different sampling rates of typical sources of information present in distribution networks. Such information comes from smart meters, supervisory control and data acquisition (SCADA) measurements, phasor measurement units and typical load profiles from pseudo measurements. The temporal aspect of the measurement set is incorporated in the estimation process by using a sampling layer concept, dealing separately with each group of measurements according to the respective updating rate. A Bayesian information fusion procedure provides the final estimation. The proposed DSSE consists in a multiple stage estimator that combines a prior model for the state variables, updated by new observations from measured values in each sampling layer, through Maximum a Posteriori estimation. This work also introduces an orthogonal method for the information fusion numerical solution, to tackle the severe ill-conditioning associated with practical distribution systems. Simulations with IEEE distribution test feeders and a Brazilian real distribution feeder illustrate the features of the proposed DSSE and its applicability. By exploring the concept of credibility intervals, the method is able to detect events on the grid, such as subtle load variation and contingencies, while maintaining accuracy. © 2010-2012 IEEE.

2022

A Data-driven Approach to Estimate the Flexibility Maps in Multiple TSO-DSO Connections

Autores
Silva, J; Sumaili, J; Silva, B; Carvalho, L; Retorta, F; Staudt, M; Miranda, V;

Publicação
IEEE Transactions on Power Systems

Abstract

2022

Sliding-Priors for Bayesian Information Fusion in SCADA plus PMU-based State Estimation

Autores
Camoes, F; Massignan, JAD; Miranda, V; London, JBA;

Publicação
2022 17TH INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)

Abstract
This paper describes a new development within the conceptual framework BAYSE (Bayesian State Estimation), which enables the full integration of the SCADA (Supervisory Control and Data Acquisition) data with PMU (phasor measurement units) data. It is based on Bayesian inference principles and extends the concept of the prior distributions to accommodate a broad set of past state conditions, under a sliding window approach. By choosing an appropriate window length, the method enhances accuracy under stationary conditions, with a reduced impact under system changes. The work also submits a rectangular coordinates transformation procedure, based on the Jacobian method, to consistently integrate polar coordinates estimations with the PMU linear model (in rectangular coordinates). The paper presents the new approach in proof-of concept mode over a didactic test-bed, using real PMU time series, to emphasize the enhanced accuracy and good asymptotic properties.

2021

Forecasting Energy Technology Diffusion in Space and Time: Model Design, Parameter Choice and Calibration

Autores
Heymann, F; vom Scheidt, F; Soares, FJ; Duenas, P; Miranda, V;

Publicação
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY

Abstract

Teses
supervisionadas

2021

Resilience Enhancement Solutions for Distribution Networks

Autor
Inês Maria Afonso Trigo de Freitas Alves

Instituição
UP-FEUP

2021

Desenvolvimento de módulos de logística hospitalar com recurso a NET Core, Oracle e Apache Cordova

Autor
Cátia Sofia Fins Machado

Instituição
UTAD

2021

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

Autor
Rodrigo Gonçalves de Morais

Instituição
UP-FEUP

2021

BIFANA - Bayesian Information Fusion in Advanced Network Assessment

Autor
Francisco Manuel Pinto Nunes da Silva Camões

Instituição
UP-FEUP

2021

cooperative and distributed location-based decision support

Autor
Artur Jorge da Silva Rocha

Instituição
UP-FEUP