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About

I was born in 1988 in Amarante, Portugal. I received the M.Sc. degree in Electrical and Computers Engineering from the Faculty of Engineering, University of Porto (FEUP), Portugal, in 2011 with major studies in Energy and specialization in Renewable Energies.

I have been working as a researcher in the R&D Centre for Power and Energy Systems (CPES) of Institute for Systems and Computer Engineering – Technology and Science (INESC TEC), Portugal, since March 2012. My research interests include integration of renewable energy sources, smart grids, protection power systems and power system state estimation.

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Details

Details

  • Name

    Henrique Silva Teixeira
  • Cluster

    Power and Energy
  • Role

    Researcher
  • Since

    02nd November 2009
008
Publications

2019

On the development of a framework for the advanced monitoring of LV grids

Authors
Kotsalos, K; Marques, L; Sampaio, G; Pereira, J; Gouveia, C; Teixeira, H; Fernandes, R; Campos, F;

Publication
2019 International Conference on Smart Energy Systems and Technologies (SEST)

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.

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.

2015

Exploiting autoencoders for three-phase state estimation in unbalanced distributions grids

Authors
Pereira Barbeiro, PNP; Teixeira, H; Krstulovic, J; Pereira, J; Soares, FJ;

Publication
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
The three-phase state estimation algorithms developed for distribution systems (DS) are based on traditional approaches, requiring components modeling and the complete knowledge of grid parameters. These algorithms are capable of dealing with the particular characteristics of DS but cannot be used in cases where grid topology and parameters are unknown, which is the most common situation in existing low voltage grids. This paper presents a novel three-phase state estimator for DS that enables the explicit estimation of voltage magnitudes and phase angles in all phases, neutral, and ground wires even when grid topology and parameters are unknown. The proposed approach is based on the use of auto-associative neural networks, the autoencoders (AE), which only require an historical database and few quasi-real-time measurements to perform an effective state estimation. Two test cases were used to evaluate the algorithm's performance: a low and a medium voltage grid. Results show that the algorithm provides accurate results even without information about grid topology and parameters. Several tests were performed to evaluate the best AE configuration. It was found that training an AE for each network feeder leads generally to better results than having a single AE for the entire system. The same happened when different AE were trained for each network phase in comparison with a single AE for the three phases.