Power Quality Transient Detection and Characterization Using Deep Learning Techniques

Nuno M. Rodrigues, Fernando M. Janeiro and Pedro M. Ramos ()
Additional contact information
Nuno M. Rodrigues: Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal
Fernando M. Janeiro: Instituto de Telecomunicações, Universidade de Évora, 7000-671 Évora, Portugal
Pedro M. Ramos: Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisboa, Portugal

Energies, 2023, vol. 16, issue 4, 1-11

Abstract: Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.

Keywords: power quality monitoring; power grid measurements; transient detection; transient characterization; deep learning; convolutional neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/4/1915/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/4/1915/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:4:p:1915-:d:1069004

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().