Water wave optimization with deep learning driven smart grid stability prediction

Mustafa Hilal, Anwer and Hassan Abdalla Hashim, Aisha and G. Mohamed, Heba and Alamgeer, Mohammad and K. Nour, Mohamed and Abdelrahman, Anas and Motwakel, Abdelwahed (2022) Water wave optimization with deep learning driven smart grid stability prediction. Computers, Materials & Continua, 73 (3). pp. 6019-6035. ISSN 1546-2226

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Abstract

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

Item Type:	Article (Journal)
Uncontrolled Keywords:	Smart grid; stability prediction; deep learning; energy systems; machine learning; metaheursitic
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101 Telecommunication. Including telegraphy, radio, radar, television
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Aisha Hassan Abdalla Hashim
Date Deposited:	12 Sep 2022 08:48
Last Modified:	12 Sep 2022 08:56
URI:	http://irep.iium.edu.my/id/eprint/99925

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