Capacity-based service restoration using Multi-Agent technology and ensemble learning

Nelson Fabian Avila, Von Wun Soo, Wan Yu Yu, Chia Chi Chu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Reliable and efficient distributed algorithms for power restoration are essential for self-healing electrical smart grids. Therefore, this paper presents a Multi-Agent System (MAS) for automatic restoration in power distribution networks. Moreover, as electrical demand fluctuates on the hourly and daily basis, an ensemble learning algorithm has been adopted for short-term forecasting of electrical energy demand. The prediction methodology is incorporated into the restoration algorithm in order to obtain a capacity-based restoration solution. Experiments carried out in two electrical networks demonstrate the importance and accuracy of the demand prediction algorithm and the feasibility of the MAS for system reconfiguration in decentralized power utilities.

Original languageEnglish
Title of host publication2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509001903
DOIs
StatePublished - 10 11 2015
Externally publishedYes
Event18th International Conference on Intelligent System Application to Power Systems, ISAP 2015 - Porto, Portugal
Duration: 11 09 201517 09 2015

Publication series

Name2015 18th International Conference on Intelligent System Application to Power Systems, ISAP 2015

Conference

Conference18th International Conference on Intelligent System Application to Power Systems, ISAP 2015
Country/TerritoryPortugal
CityPorto
Period11/09/1517/09/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Automatic Power Restoration
  • Distributed Artificial Intelligence
  • Ensemble Learning
  • Short-Term Demand Forecasting

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