Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Associate Professor since 2011 at the Faculty of Engineering of the University of Porto (FEUP).

PhD obtained in 1995 in Electrical Engineering and Computers at FEUP.

Licenciado in 1984 in Electrical Engineering and Computers at FEUP.

Researcher at INESC TEC since 1985.

Interest
Topics
Details

Details

  • Name

    José Nuno Fidalgo
  • Cluster

    Power and Energy
  • Role

    Senior Researcher
  • Since

    25th June 1985
042
Publications

2022

Identification of Typical and Anomalous Patterns in Electricity Consumption

Authors
Fidalgo, JN; Macedo, P;

Publication
APPLIED SCIENCES-BASEL

Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries’ socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

2022

Decision support system for long-term reinforcement planning of distribution networks

Authors
Fidalgo, JN; Azevedo, F;

Publication
ELECTRIC POWER SYSTEMS RESEARCH

Abstract
The last decade has witnessed a growing tendency to promote deeper exploitation of power systems infrastructure, postponing investments in networks reinforcement. In particular, the literature on smart grids research often emphasizes their potential to defer investments. The study reported in this paper analyses the impact of reinforcement decisions, comparing the long-term costs associated with different network conditions and economic analysis parameters. The results support the conclusion that network reinforcement deferral is not a panacea, as it often generates costly situations in the long-term. The challenge is not to find new ways to postpone investments, but to find the most beneficial criterion to trigger the grid reinforcements actions. Another contribution of the present work is a decision support system to identify the most economical network reinforcement criterion in terms of the peak to capacity ratio.

2021

Non-Intrusive Load Monitoring for Household Disaggregated Energy Sensing

Authors
Paulos J.P.; Nuno Fidalgo J.; Gama J.;

Publication
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Abstract

2021

Detection and Mitigation of Extreme Losses in Distribution Networks

Authors
Paulos J.P.; Fidalgo J.N.; Saraiva J.T.; Barbosa N.;

Publication
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Abstract

2021

Estimation of the Global Amount of Mandatory Investments for Distribution Network Expansion Planning

Authors
MacEdo P.M.; Fidalgo J.N.; Saraiva J.T.;

Publication
2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings

Abstract

Supervised
thesis

2021

Previsão de consumo de médio e longo prazo

Author
André Marques Rodrigues

Institution
UP-FEUP

2021

Knowledge Graphs for Ubiquitous Datasets

Author
Hugo Manuel Soares Oliveira

Institution
UP-FCUP

2021

Previsão de preços de mercado baseada em Deep Learning

Author
Ana Rita Martins Cruz e Silva

Institution
UP-FEUP

2021

Gestão e Atualização Automática de Firmware para Câmaras de Videovigilância em Shop Floor

Author
LUÍS MIGUEL PINTO LISBOA

Institution
IPP-ISEP

2021

Linguistic and Emotion-based Identification of Tweets with Fake News: A Case Study

Author
Vitor Sexto Bernardes

Institution
UP-FCUP