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About

About

I obtained my M.Sc. degree in electrical engineering at the Faculty of Engineering of University of Porto (FEUP), Porto, in 2010. In the same year I joined INESC TEC as researcher at the Power Systems Unit. Since then, I have worked in several European and National financed projects focused on the fields of electric vehicles, smart grids, distribution generation, metering and management of energy consumption, FACTS, storage devices, state estimation, among others in power systems area. I have also participated in several consultancy projects for the Portuguese TSO and DSO related with the integration of renewable resources in transmission and distribution networks. Currently, my main research activities are focused on the area of smart grids, namely on the developments of  tools for monitoring distribution networks in real-time using state estimation algorithms based on artificial intelligence.

Interest
Topics
Details

Details

  • Name

    Pedro Pereira Barbeiro
  • Cluster

    Power and Energy
  • Role

    Researcher
  • Since

    01st March 2010
Publications

2017

LV state estimation and TSO–DSO cooperation tools: results of the French field tests in the evolvDSO project

Authors
Viania Sebastian, M; Caujolle, M; Goncer Maraver, B; Pereira, J; Sumaili, J; Barbeiro, P; Silva, J; Bessa, R;

Publication
CIRED - Open Access Proceedings Journal

Abstract

2016

A state estimator for LV networks: Results from the evolvDSO project

Authors
Teixeira, H; Pereira Barbeiro, PN; Pereira, J; Bessa, R; Matos, PG; Lemos, D; Morais, AC; Caujolle, M; Sebastian Viana, M;

Publication
IET Conference Publications

Abstract
The increasing connection of new assets in LV networks will surely require a better monitoring of these systems in a real-time manner. In order to meet this purpose, a Distribution State Estimator (DSE) module clearly appears as the most cost-effective and possibly the only reliable option available. In this sense, in the scope of the evolvDSO project, a DSE tool exploiting the concept of ELM-AE was developed and tested in two distinct real LV distribution networks. In this paper the main results achieved with the proposed DSE tool are presented and discussed.

2016

LV SCADA project: In-field validation of a distribution state estimation tool for LV networks

Authors
Barbeiro, P; Pereira, J; Teixeira, H; Seca, L; Silva, P; Silva, N; Melo, F;

Publication
IET Conference Publications

Abstract
The LV SCADA project aimed at the development of advanced technical, commercial and regulatory solutions to contribute for an effective smart grid implementation. One of the biggest challenges of the project was related with the lack of characterization that usually exists in LV networks, together with the almost non-existing observability. In order to overcome these issues, a LV management system integrating a state estimation tool based on artificial intelligence techniques was developed. The tool is currently installed in one pilot demonstration site that aggregates 2 MV/LV substations. In this paper the performance of tool in real environment is evaluated and the results gathered from the pilot site are analyzed.

2016

Active Management of Electric Vehicles Acting as Distributed Storage

Authors
Soares, FJ; Almeida, PMR; Galus, M; Barbeiro, PNP; Peças Lopes, J;

Publication
Smart Grid Handbook

Abstract

2015

An ELM-AE State Estimator for Real-Time Monitoring in Poorly Characterized Distribution Networks

Authors
Pereira Barbeiro, PNP; Teixeira, H; Pereira, J; Bessa, R;

Publication
2015 IEEE EINDHOVEN POWERTECH

Abstract
In this paper a Distribution State Estimator (DSE) tool suitable for real-time monitoring in poorly characterized low voltage networks is presented. An Autoencoder (AE) properly trained with Extreme Learning Machine (ELM) technique is the "brain" of the DSE. The estimation of system state variables, i.e., voltage magnitudes and phase angles is performed with an Evolutionary Particle Swarm Optimization (EPSO) algorithm that makes use of the already trained AE. By taking advantage of historical data and a very limited number of quasi real-time measurements, the presented approach turns possible monitoring networks where information of topology and parameters is not available. Results show improvements in terms of estimation accuracy and time performance when compared to other similar DSE tools that make use of the traditional back-propagation based algorithms for training execution.