Open AccessMachine learning based energy management system for grid disaster mitigation
Author(s) -
Maharjan Lizon,
Ditsworth Mark,
Niraula Manish,
Caicedo Narvaez Carlos,
Fahimi Babak
Publication year - 2019
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/iet-stg.2018.0043
Subject(s) - distributed generation , computer science , smart grid , electric power system , distributed computing , resilience (materials science) , grid , electricity , process (computing) , natural disaster , electronics , systems engineering , power (physics) , engineering , renewable energy , electrical engineering , physics , geometry , mathematics , quantum mechanics , meteorology , thermodynamics , operating system
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.