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

Utility grids: (a) conventional grid (b) smart grid.

Utility grids: (a) conventional grid (b) smart grid. In the conventional system power flows from in one direction only; but for smart grid, there is no strict structure. Generation can occur at the consumer side too, such as the wind and the solar farms at the outer periphery of the topology. Power flow can also be bidirectional, demonstrated by the energy storages and the house in this illustration.

 

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

A comprehensive study is conducted 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. 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.

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

 

IoT architecture. Data collected by sensors can be sent to
different systems which use various software platforms to carry out
intended tasks.

Structure for IoT implemented layers for SGs. Each IoT
layer corresponds to a certain layer of SG infrastructure.

System architecture for load shedding and smart load
controlling algorithm. All the components of the system are
connected to the cloud which makes decisions based on the system
inputs, and sends out commands for execution. The directions of the
arrows indicate the flow of data.

Big data characteristics: large volume of data with lots of
variations which are generated, stored, or transmitted at a high at a high velocity can be labeled as big data.

The components that create big data analytics. Big data and the techniques to analyze it has created the discipline of big data analytics.

Application of machine learning in smart grid security.
Unsupervised and supervised both approaches can be used to carry out an array of tasks including threat identification and data categorization.

Security concerns of IoT integrated SG system.

These can be categorized into four major types: physical threat, network threat, software threat, and encryption threat. The security concerns under each type are marked in corresponding color.

Attacks on major power grid components during 1994-2004;
the transmission system faced most of the attacks, reaching 62%.

 

Layered security framework for smart grids.

Layered security framework for smart grids.

This comprehensive approach considers security at each stage of the infrastructure, rather than only smart meter placed at consumer location. Strategic direction and technical execution forms the two major contributors in defining the framework responsibilities.

 

Source

Application of Big Data and Machine Learning in Smart Grid and Associated Security Concerns: A Review (pdf) -IEEE

 





 

 
   
 
 
   
 
   
 
   
   
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Last modified: June 20, 2022