Cookies Policy
We use cookies to improve our site and your experience. By continuing to browse our site you accept our cookie policy. Find out More
Close
  • Menu
About

About

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)

Interest
Topics
Details

Details

005
Publications

2018

Technical-economic analysis for the integration of PV systems in Brazil considering policy and regulatory issues

Authors
Vilaca Gomes, PV; Knak Neto, NK; Carvalho, L; Sumaili, J; Saraiva, JT; Dias, BH; Miranda, V; Souza, SM;

Publication
Energy Policy

Abstract

2018

State estimation pre-filtering with overlapping tiling of autoencoders

Authors
Saran, MAM; Miranda, V;

Publication
Electric Power Systems Research

Abstract
This paper presents a new concept for an approach to deal with measurements contaminated with gross errors, prior to power system state estimation. Instead of a simple filtering operation, the new procedure develops a screen-and-repair process, going through the phases of detection, identification and correction of multiple gross errors. The method is based on the definition of the coverage of the measurement set by a tiling scheme of 3-overlapping autoencoders, trained with denoising techniques and correntropy, that produce an ensemble-like set of three proposals for each measurement. These proposals are then subject to a process of fusion to produce a vector of proposed/corrected measurements, and two fusion methods are compared, with advantage to the Parzen Windows method. The original measurement vector can then be recognized as clean or diagnosed with possible gross errors, together with corrections that remove these errors. The repaired vectors can then serve as input to classical state estimation procedures, as only a small noise remains. A test case illustrates the effectiveness of the technique, which could deal with four simultaneous gross errors and achieve a result close to full recognition and correction of the errors. © 2017 Elsevier B.V.

2018

Hybrid systems control applied to wind power forecasting deviation considering PHS

Authors
Rezende, I; Silva, JM; Miranda, V; Freitas, V; Dias, BH;

Publication
SBSE 2018 - 7th Brazilian Electrical Systems Symposium

Abstract
This paper proposes a methodology using Hybrid Control System (HS) to manage the integration of Variable Renewable Electricity Sources (VRES). The HS define a combination of discrete and continuous signals, in this case, discrete by Pump-Hydro-Storage (PHS) and continuous performance is the Wind Power (WP). The coupling of Wind Power and PHS to produce a dispatchable power output could be a significant benefit to those in an energy trading system. Improving VRES prediction reduces system dispatch errors, however does not give total economic opportunities to the generator. Increased dispatchable backup power generation can improve the system's ability to handle deviations of WP, as verified when the proposed approach is applied to Brazilian and Portuguese power system. © 2018 IEEE.

2018

Identifying topology in power networks in the absence of breaker status sensor signals

Authors
Oliveira, R; Bessa, R; Iranda, VM;

Publication
19th IEEE Mediterranean Eletrotechnical Conference, MELECON 2018 - Proceedings

Abstract
This paper presents the concept of a tapered deep neural network, subject to unsupervised training layer by layer, under a criterion of maximum entropy, to perform the estimation of breaker status in the absence of a specific sensor signal. The almost perfect prediction power of the model confirms the conjecture that the knowledge of the topology of a network is hidden in the electric measurement values in the network. A test case is presented with computing speed accelerated by using a GPU (graphics processing unit). The comparison with a previous model illustrates the superiority of the novel approach. © 2018 IEEE.

2018

The challenges of estimating the impact of distributed energy resources flexibility on the TSO/DSO boundary node operating points

Authors
Silva, J; Sumaili, J; Bessa, RJ; Seca, L; Matos, M; Miranda, V;

Publication
Computers and Operations Research

Abstract
The increasing penetration of renewable energy sources characterized by a high degree of variability and uncertainty is a complex challenge for network operators that are obligated to ensure their connection while keeping the quality and security of supply. In order to deal with this variable behavior and forecast uncertainty, the distribution networks are equipped with flexible distributed energy resources capable of adjusting their operating point to avoid technical issues (voltage problems, congestion, etc.). Within this paradigm, the flexibility that, in fact, can be provided by such resources, needs to be estimated/forecasted up to the transmission network node (primary substation) and requires new tools for TSO/DSO coordination. This paper addresses this topic by developing a methodology capable of finding the flexibility area while taking into account the technical grid constraints. The proposed approach is based on the formulation of a single optimization problem which is run several times, according with the expected precision for the flexibility area estimation. To each optimization problem run, a different objective function belonging to a family of straight lines is assigned. This allows exploring the active and reactive power flow limits at the TSO/DSO boundary nodes - which define the flexibility area. The effectiveness of the proposed model has been evaluated on two test networks and the results suggest a step forward in the TSO/DSO coordination field. Nevertheless, further investigations to study the effect of assets with discrete control nature (e.g., on load tap changers - OLTC, capacitor banks) on the occurrence of disjoint flexibility areas should be carried. © 2017 Elsevier Ltd.

Supervised
thesis

2018

Análise de Risco na Formação das Decisões de pré-despacho em Sistemas com Elevada Penetração Eólica

Author
Mauro Sérgio Silva Pinto

Institution
Outra

2017

State Estimation for Evolving Power Systems Paradigms

Author
Gil da Silva Sampaio

Institution
UP-FEP

2017

Towards Dynamic State Estimation: Extending State Estimation to Dynamic State Recognition Through Knowledge Discovery With Deep Learning

Author
Shabnam Pesteh

Institution
UP-FEUP

2017

Deep Learning Applied to PMU Data in Power Systems

Author
Pedro Emanuel Almeida Cardoso

Institution
UP-FEUP

2017

Training autoencoders for state estimation in smart grids

Author
Rui Miguel Machado Oliveira

Institution
UP-FEUP