Identification of natural disaster impacted electricity load profiles with k means clustering algorithm

Simon Hedegård Jessen*, Zheng Grace Ma, Francisco Danang Wijaya, Juan Vasquez, Josep Guerrero, Bo Nørregaard Jørgensen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Natural disasters threat the resilience of the electricity system. However, little literature has investigated the electricity system’s recovering process and progress after natural disasters’ hit which strongly influence the system operators’ planning and quality of the security of supply for the electricity customers. To fill the research gap, this paper applies an unsupervised machine learning method, the k means clustering algorithm, to investigate the normal/abnormal electricity load profiles, identify natural disaster- and electrical fault-impacted electricity load profiles with a case study of the Lombok electricity system, Indonesia, and½-hourly electricity load data from 2015 until 2021. The results show that electricity consumption in Lombok has increased over the years, which match the installed production capacity of Lombok. The results prove that the disturbance-induced electricity load patterns and especially natural disaster-impacted load profiles can be identified by the k means clustering algorithm. Especially, the pre-, during, and post-natural disaster impacted load patterns can be portrayed. Furthermore, the investigation results regarding the impacts of natural disasters and electrical faults on the performance of the electricity system, show that the natural disaster-induced load reductions and electrical fault-induced load reductions differ from the short and long-term perspectives. Moreover, the results can facilitate the electricity system operators to better understand the load patterns, predict ND strikes’ impact on the electricity system and conduct better long-term energy management strategies.
Original languageEnglish
Article number59
JournalEnergy Informatics
Volume5
Issue numberSuppl. 4
Number of pages29
ISSN2520-8942
DOIs
Publication statusPublished - 2022
EventEnergy Informatics.Academy Conference 2022 - Dandy Business Park, Vejle, Denmark
Duration: 24. Aug 202225. Aug 2022
https://www.energyinformatics.academy/eia-2022-conference

Conference

ConferenceEnergy Informatics.Academy Conference 2022
LocationDandy Business Park
Country/TerritoryDenmark
CityVejle
Period24/08/202225/08/2022
Internet address

Keywords

  • Climate resilience
  • unsupervised learning
  • k means clustering
  • electricity grid
  • electricity load profile
  • natural disaster

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