Machine learning based energy management system for grid disaster mitigation. Issue 2 (7th February 2019)

• Record Type: Journal Article
• Authors: Maharjan, Lizon; Ditsworth, Mark; Niraula, Manish; Caicedo Narvaez, Carlos; Fahimi, Babak
• Is Part Of: IET smart grid. Volume 2:Issue 2(2019)
• Page Start: 172
• Page End: 182
• Publication Date: 2019-02-07
• Subjects: energy management systems -- disasters -- power engineering computing -- building management systems -- power grids -- distributed power generation -- learning (artificial intelligence) -- smart power grids; machine learning based energy management system -- grid disaster mitigation -- recent increase -- infiltration -- distributed resources -- traditional operation -- power systems -- recent natural disasters -- resilience -- power infrastructure -- electricity dependent community -- resilient smart grid network -- power availability -- disastrous events -- power electronics -- load categorisation features -- presented system utilises ML tools -- smart grid design process; B0260 Optimisation techniques -- B8110B Power system management, operation and economics -- C6170K Knowledge engineering techniques -- C7410B Power engineering computing; Smart power grids -- Periodicals; Computer science -- Periodicals; Energy industries -- Periodicals; Broadcasting -- Periodicals; 333.79110285
• DOI: 10.1049/iet-stg.2018.0043
• Languages: English
• ISSNs: 2515-2947

Abstract

Abstract : 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.