Micro Software Defined Control (μSDC): Empowering Smart Grids With Enhanced Control and Optimization

被引:0
|
作者
Al Mhdawi, Ammar K. [1 ]
Al-Raweshidy, Hamed [2 ]
Al-Karkhi, Wars J. [3 ]
Jaleel Humaidi, Amjad [3 ]
机构
[1] Montfort Univ, Sch Engn & Sustainable Dev, Dept Control Engn, Leicester LE1 9BH, Leics, England
[2] Brunel Univ London, Dept Elect & Elect Engn, London UB8 3PH, England
[3] Univ Technol Iraq, Dept Control & Syst Engn, Baghdad 10066, Iraq
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Software-defined control; neural networks; congestion control; power consumption; smart meters;
D O I
10.1109/ACCESS.2024.3421947
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:95750 / 95761
页数:12
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