Data Analysis Techniques Applied to Distribution Systems: A Systematic Mapping Study

Abstract

The increasing electricity demand has led the conventional distribution system to evolve into the smart grid context. One of the advantages of this intelligent system is the ability to estimate the states of a grid. Implementing a state estimator in distribution networks requires much analysis – such as the observability of an extensive system, big data techniques for data processing, and the use of artificial intelligence to generate predictions and protect the control system against cyber-attacks. This systematic mapping study covers 15148 articles, selecting 37 for further research and discussion. The reports offer different state estimators and techniques to improve performance, for instance, solutions against malicious data, optimal meter allocation, and voltage control. In addition to these topics, this article discusses different approaches to generate pseudo measurements. Probabilistic and deterministic, machine learning, and neural network models appear as a trend. The studies show the main methods used for data collection, offering a trend toward smart meters.

Keywords:

• state estimator
• smart grid
• distribution system
• big data
• artificial intelligence
• machine learning

ACKNOWLEDGEMENTS

We would also like to thank the Unisinos University (http://www.unisinos.br/global/en/).

Additional information

Funding

This work was financed in part by FAPERGS/Brazil (Foundation for the Supporting of Research in the State of Rio Grande do Sul – http://www.fapergs.rs.gov.br), CNPq/Brazil (National Council for Scientific and Technological Development – http://www.cnpq.br), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001 and CEEE Equatorial Power Utility https://www.ceee.com.br/aceee/distribuicao.

Notes on contributors

Ricardo dos Santos Costa

Ricardo dos Santos Costa received an M.Sc. in electrical engineering at the University of Vale do Rio dos Sinos (Unisinos) in 2022. He received his Bachelor’s degree in Mathematics and Bachelor’s in Electrical Engineering, both from Unisinos. His research area includes data science, artificial intelligence, smart grids, power systems, and automation.

Jorge Arthur Schneider Aranda

Jorge Arthur Schneider Aranda is a Researcher at the University of Vale do Rio dos Sinos (UNISINOS). Doctoral candidate in Applied Computing and Master’s degree in applied computing at the University of Vale dos Sinos, Graduated in Computer Science at the Feevale University. He has experience in machine learning, networks, Internet of Things, e-health, and ubiquitous computing.

Vitor Werner de Vargas

Vitor Werner de Vargas is a Master’s student, and researcher in Applied Computing at the University of Vale do Rio dos Sinos (Unisinos). He received his Bachelor’s degree in Electrical Engineering from Unisinos in 2020. His research interests include data analysis, machine learning, automated systems, smart grids, and power systems.

Paulo Ricardo da Silva Pereira

Paulo Ricardo da Silva Pereira is a professor at University of Vale do Rio dos Sinos (Unisinos). He has worked with Power Systems since 1998 at Certaja, RGE, and CEEE. He received his M.Sc. and Ph.D. in Electrical Engineering from the Federal University of Santa Maria, Brazil, in 2009 and 2014, respectively. His research interests include power systems, renewable and distributed energy resources, power quality, embedded systems, and automation.

Jorge Luis Victória Barbosa

Jorge Luis Victória Barbosa received an M.Sc. and Ph.D. in computer science from the Federal University of Rio Grande do Sul, Brazil. He conducted post-doctoral studies at Sungkyunkwan University (South Korea) and the University of California Irvine (USA). Jorge is a full professor at the University of Vale do Rio dos Sinos (UNISINOS), Brazil. His research interests include electric power components and systems.

Marcelo Pinto Vianna

Marcelo Pinto Vianna is an electrical engineer at Equatorial Energia. He has worked with power systems since 2004 at RGE, CEEE, and Equatorial. He received a master’s degree in electrical engineering from the Pontifical Catholic University of Rio Grande do Sul, Brazil, in 2009. His research interests include power systems, renewable and distributed energy resources, and power quality.