Please use this identifier to cite or link to this item:
Title: Micro Software Defined Control (µSDC): Empowering Smart Grids with Enhanced Control and Optimization
Authors: Al Mhdawi, AK
Al-Raweshidy, H
Al-Karkhi, WJ
Humaidi, AJ
Keywords: software-defined control;neural networks;congestion control;power consumption;smart meters
Issue Date: 2-Jul-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Al Mhdawi, A.K. et al. (2024) 'Micro Software Defined Control (µSDC): Empowering Smart Grids with Enhanced Control and Optimization', IEEE Access, 0 (early access), pp. 1 - 13. doi: 10.1109/ACCESS.2024.3421947.
Abstract: It has become a fundamental component of the electrical networking system, both in residential and industrial settings, to adopt advanced power meter architecture. With traditional smart meters, a bi-channel communication network is established between homes and utility companies, providing consumers with information regarding their daily power consumption. Manual meter reading, however, may result in inaccurate meter logging and incorrect billing criteria, which will lead to an increase in overhead costs associated with deploying meter readers and billing power consumption for each site within metropolitan and large urban areas. Moreover, the current smart metering system does not enable consumers to predict their future energy consumption, only providing insights into their current power consumption and accumulative costs. In order to address these issues, we propose a novel intelligent Software-Defined Control (SDC) super cluster with a comprehensive architecture based on SDN routing capabilities, which differs from conventional commercial smart meters. The developed micro cluster is enhanced to run full availability and high performance compared to traditional metering system. Moreover it deploys intelligent capabilities to predict the consumption of power per home, we implemented a polynomial model experimentally. Furthermore, we propose an intelligent Software-Defined Controller Gateway (SDN-GW) to serve as a traffic predictor between distributed metering nodes and the cloud data warehouse, eliminating congestion caused by the large volumes of traffic data generated periodically by the metering nodes. Based on the experimental results, the software-defined control system was estimated to have 97.75% percent accuracy in power prediction, and the traffic flow predictor demonstrated 98.79% percent accuracy in network traffic prediction. Furthermore, the proposed SDN-GW achieved 29.37% power consumption rate compared to standard routing engine.
Other Identifiers: ORCiD: Hamed Al-Raweshidy
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

Files in This Item:
File Description SizeFormat 
FullText.pdf© Copyright 2024 The Authors. Published under license by Institute of Electrical and Electronics Engineers (IEEE). This work is licensed under a Creative Commons Attribution 4.0 License.. For more information, see MBAdobe PDFView/Open

This item is licensed under a Creative Commons License Creative Commons