Open AccessData reduction algorithm for correlated data in the smart grid
Author(s) -
Pourmirza Zoya,
Walker Sara,
Brooke John
Publication year - 2021
Publication title -
iet smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.612
H-Index - 11
ISSN - 2515-2947
DOI - 10.1049/stg2.12010
Subject(s) - computer science , smart grid , algorithm , data transmission , reduction (mathematics) , data reduction , data compression , data mining , real time computing , database , distributed computing , computer network , engineering , electrical engineering , geometry , mathematics
Smart grids are intelligent electrical networks that incorporate information and communication technology (ICT) to provide data services for the power grid. In this paper, the ICT requirements for monitoring and control of the neighbourhood area network level of the smart grid, with particular emphasis on making the ICT infrastructure energy efficient, are analysed. One approach to provide energy efficiency in the communication system is to develop a data reduction algorithm to reduce the volume of data prior to transmission. Thus, a data compression technique called DRACO (data reduction algorithm for correlated data) that shows a reasonable compression ratio while using network resources efficiently is designed and developed. DRACO can be applied to data with a high data sampling rate, and can transmit the essential information with compression ratios of 70%–99%. The results of applying DRACO on real data collected by devices located in the University of Manchester campus are discussed, followed by the evaluation and validation of DRACO by comparing it with other available techniques. Finally, it is concluded that DRACO is suitable for smart grid applications since it optimizes the network resource consumption and reduces the communication energy cost while maintaining the integrity and quality of data.