Machine learning based energy management system for grid disaster mitigation

The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solu...

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Bibliographic Details
Published in:IET smart grid 2019-06, Vol.2 (2), p.172-182
Main Authors: Maharjan, Lizon, Ditsworth, Mark, Niraula, Manish, Caicedo Narvaez, Carlos, Fahimi, Babak
Format: Article
Language:English
Subjects:
Algorithms
B0260 Optimisation techniques
B8110B Power system management, operation and economics
building management systems
C6170K Knowledge engineering techniques
C7410B Power engineering computing
Controllers
Design
disasters
disastrous events
Distributed generation
distributed power generation
distributed resources
Electric power distribution
Electric power systems
electricity dependent community
Electronics
Energy economics
Energy management
energy management systems
Energy sources
grid disaster mitigation
infiltration
learning (artificial intelligence)
load categorisation features
Machine learning
machine learning based energy management system
Natural disasters
power availability
power electronics
power engineering computing
power grids
power infrastructure
power systems
POWER TRANSMISSION AND DISTRIBUTION
presented system utilises ML tools
recent increase
recent natural disasters
Resilience
resilient smart grid network
Smart grid
smart grid design process
smart power grids
traditional operation
Wavelet transforms
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Summary:The recent increase in infiltration of distributed resources has challenged the traditional operation of power systems. Simultaneously, devastating effects of recent natural disasters have questioned the resilience of power infrastructure for an electricity dependent community. In this study, a solution has been presented in the form of a resilient smart grid network which utilises distributed energy resources (DERs) and machine learning (ML) algorithms to improve the power availability during disastrous events. In addition to power electronics with load categorisation features, the presented system utilises ML tools to use the information from neighbouring units and external sources to make complicated logical decisions directed towards providing power to critical loads at all times. Furthermore, the provided model encourages consideration of ML tools as a part of smart grid design process together with power electronics and controls, rather than as an additional feature.
ISSN:2515-2947
2515-2947
DOI:10.1049/iet-stg.2018.0043