Reducing Detrimental Communication Failure Impacts in Microgrids by Using Deep Learning Techniques.
Sensors (Basel)
; 22(16)2022 Aug 11.
Article
in En
| MEDLINE
| ID: mdl-36015769
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Deep Learning
Type of study:
Prognostic_studies
Language:
En
Journal:
Sensors (Basel)
Year:
2022
Document type:
Article
Affiliation country:
Denmark