Evidence map›Paper›PMID 36015769›Full record

ArticleSensors (Basel, Switzerland)2022

Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques.

Babak Arbab-Zavar, Suleiman M Sharkh, Emilio J Palacios-Garcia, Juan C Vasquez, Josep M Guerrero

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 34% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Babak Arbab-ZavarAAU Energy, Aalborg University, DK-9220 Aalborg, Denmark.ORCID 0000-0002-3332-0508
Suleiman M SharkhFaculty of Engineering and the Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK.ORCID 0000-0001-7335-8503
Emilio J Palacios-GarciaDepartment of Electrical Engineering (ESAT), KU Leuven, ELECTA, BE-3001 Leuven, Belgium.ORCID 0000-0003-2703-6532
Juan C VasquezAAU Energy, Aalborg University, DK-9220 Aalborg, Denmark.ORCID 0000-0001-6332-385X
Josep M GuerreroAAU Energy, Aalborg University, DK-9220 Aalborg, Denmark.ORCID 0000-0001-5236-4592
Aalborg University · DKKU Leuven · BEUniversity of Southampton · GB

Funding

The Velux Foundations 25920
6 · The paper itself

Abstract

A Microgrid (MG), like any other smart and interoperable power system, requires device-to-device (D2D) communication structures in order to function effectively. This communication system, however, is not immune to intentional or unintentional failures. This paper discusses the effects of communication link failures on MG control and management and proposes solutions based on enhancing message content to mitigate their detritus impact. In order to achieve this goal, generation and consumption forecasting using deep learning (DL) methods at the next time steps is used. The architecture of an energy management system (EMS) and an energy storage system (ESS) that are able to operate in coordination is introduced and evaluated by simulation tests, which show promising results and illustrate the efficacy of the proposed methods. It is important to mention that, in this paper, three dissimilar topics namely MG control/management, DL-based forecasting, and D2D communication architectures are employed and this combination is proven to be capable of achieving the aforesaid objective.

Indexed as

Deep LearningCommunicationComputer SimulationForecastingartificial neural networksdeep learningmachine-to-machine communicationmicrogridtime series forecasting

Identifiers

PMID36015769
PMCPMC9416427
OpenAlexW4291123818

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.