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Transformation of smart grid using machine learning

conference contribution
posted on 20.10.2020, 00:00 by Salahuddin AzadSalahuddin Azad, Fariza SabrinaFariza Sabrina, Saleh WasimiSaleh Wasimi
With the advent of distributed and renewable energy sources, maintaining the stability of power grid is becoming increasingly difficult. Traditional power grid can be transformed into a smart grid by augmenting it with information and communication technologies, and machine intelligence. Machine learning and artificial intelligence can enable smart grid to make intelligent decisions and respond to sudden changes in customer demands, power outages, sudden drops and rises in renewable energy output or any other catastrophic events. Machine learning can also help capture customer consumption patterns, forecast energy demand and power generation of intermittent sources, and predict equipment failures. Reinforced learning can aid in making energy dispatch decisions and activate demand management signals in order to maintain balance of power supply and demand. The usage of wireless technologies in smart grid renders it vulnerable to cyber security threats. With the increase in data volume, it is now possible to employ machine learning for the detection and prevention of anomalous behaviour, intrusion, cyber-attacks, and malicious activities as well as data authentication. This paper reviews the application of different machine learning approaches that aims at enhancing the stability, reliability, security, efficiency and responsiveness of smart grid. This paper also discusses some of the challenges in implementing machine learning solutions for smart grid.

History

Start Page

1

End Page

6

Number of Pages

6

Start Date

26/11/2019

Finish Date

29/11/2019

ISBN-13

9781728150444

Location

Nadi, Fiji

Publisher

IEEE

Place of Publication

Online

Peer Reviewed

Yes

Open Access

No

Era Eligible

Yes

Name of Conference

29th Australasian Universities Power Engineering Conference (AUPEC 2019)