Water Wave Optimization with Deep Learning Driven Smart Grid Stability Prediction

Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2022, Vol.73 (3), p.6019-6035
Main Authors: Mustafa Hilal, Anwer, Hassan Abdalla Hashim, Aisha, G. Mohamed, Heba, Alamgeer, Mohammad, K. Nour, Mohamed, Abdelrahman, Anas, Motwakel, Abdelwahed
Format: Article
Language:English
Subjects:
Advanced metering infrastructure
Algorithms
Belief networks
Deep learning
Electric power grids
Electricity consumption
Electricity distribution
Machine learning
Optimization
Prediction models
Smart grid
Stability
Water waves
Wattmeters
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Summary:Smart Grid (SG) technologies enable the acquisition of huge volumes of high dimension and multi-class data related to electric power grid operations through the integration of advanced metering infrastructures, control systems, and communication technologies. In SGs, user demand data is gathered and examined over the present supply criteria whereas the expenses are then informed to the clients so that they can decide about electricity consumption. Since the entire procedure is valued on the basis of time, it is essential to perform adaptive estimation of the SG’s stability. Recent advancements in Machine Learning (ML) and Deep Learning (DL) models enable the designing of effective stability prediction models in SGs. In this background, the current study introduces a novel Water Wave Optimization with Optimal Deep Learning Driven Smart Grid Stability Prediction (WWOODL-SGSP) model. The aim of the presented WWOODL-SGSP model is to predict the stability level of SGs in a proficient manner. To attain this, the proposed WWOODL-SGSP model initially applies normalization process to scale the data to a uniform level. Then, WWO algorithm is applied to choose an optimal subset of features from the pre-processed data. Next, Deep Belief Network (DBN) model is followed to predict the stability level of SGs. Finally, Slime Mold Algorithm (SMA) is exploited to fine tune the hyperparameters involved in DBN model. In order to validate the enhanced performance of the proposed WWOODL-SGSP model, a wide range of experimental analyses was performed. The simulation results confirm the enhanced predictive results of WWOODL-SGSP model over other recent approaches.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2022.031425