Machine learning based energy management system for grid disaster mitigation

• 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
• ISSN: 2515-2947; 2515-2947
• DOI: 10.1049/iet-stg.2018.0043

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.