Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review

IEEE Access

Eklas Hossain¹  Sarder Shazali Sikander²  Imtiaj Khan³  Fuad Un-Noor⁴  Md. Samiul Haque Sunny⁴ 

[1] Department of Electrical Engineering and Renewable Energy, Oregon Tech, Klamath Falls, OR, USA;Department of Electrical Engineering, National University of Sciences and Technology, Islamabad, Pakistan;Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh;Department of Electrical and Electronic Engineering, Khulna University of Engineering and Technology, Khulna, Bangladesh;

关键词: Big data analysis;    cyber security;    IoT;    machine learning;    smart grid;   

DOI  :  10.1109/ACCESS.2019.2894819

来源: DOAJ

【 摘 要 】

This paper conducts a comprehensive study on the application of big data and machine learning in the electrical power grid introduced through the emergence of the next-generation power system-the smart grid (SG). Connectivity lies at the core of this new grid infrastructure, which is provided by the Internet of Things (IoT). This connectivity, and constant communication required in this system, also introduced a massive data volume that demands techniques far superior to conventional methods for proper analysis and decision-making. The IoT-integrated SG system can provide efficient load forecasting and data acquisition technique along with cost-effectiveness. Big data analysis and machine learning techniques are essential to reaping these benefits. In the complex connected system of SG, cyber security becomes a critical issue; IoT devices and their data turning into major targets of attacks. Such security concerns and their solutions are also included in this paper. Key information obtained through literature review is tabulated in the corresponding sections to provide a clear synopsis; and the findings of this rigorous review are listed to give a concise picture of this area of study and promising future fields of academic and industrial research, with current limitations with viable solutions along with their effectiveness.

【 授权许可】

Unknown