Application of Long Short-Term Memory (LSTM) Neural Network for the estimation of communication network delay in smart grid applications - Modelling and Optinal Decision for Uncertain Systems Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Application of Long Short-Term Memory (LSTM) Neural Network for the estimation of communication network delay in smart grid applications

Résumé

Vast integration of new technologies to enable smart control of the power grid requires a reliable, efficient and resilient communication infrastructure. Today, many communication protocols (e.g., IEC 61850, OPC UA, Modbus, Internet, WiMAX, 4G, Wi-Fi, etc.) and technologies (e.g., PLC, GSM, Optic fiber, RF radio mesh, Cellular, etc.) are established for the smart grid applications. In case of the stability guarantee of smart grid, the Quality of Service (QoS) is a challenge to be considered. One of the major concerns in data delivery over the network is a low latency message transmission to ensure the time critical tasks, e.g., control and protection tasks. In this context, the main contribution of this paper is to propose a model methodology for the communication network delay in smart grid applications. To be compatible with the further goal of delay predictive compensation method, the present paper proposes a message transmission delay estimation method using Long Short-Term Memory (LSTM) neural network. To this end, Python is the chosen programming language, including its required libraries for the considered application. Delay values measured on a real-time HV/MV substation application are used as input data for validation purpose.
Fichier principal
Vignette du fichier
EEEIC_234_Ronak_Feizimirkhani.pdf (451.09 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03357785 , version 1 (29-09-2021)

Identifiants

Citer

Ronak Feizimirkhani, van Hoa Nguyen, Yvon Bésanger, Quoc Tuan Tran, Antoneta Iuliana Bratcu, et al.. Application of Long Short-Term Memory (LSTM) Neural Network for the estimation of communication network delay in smart grid applications. EEEIC 2021 - 21st IEEE International Conference on Environment and Electrical Engineering (EEEIC 2021), Sep 2021, Bari, Italy. ⟨10.1109/EEEIC/ICPSEurope51590.2021.9584791⟩. ⟨hal-03357785⟩
93 Consultations
257 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More