research-article

Intelligent Decision Support System for Electric Power Restoration

Authors:

Abderrazak Khediri,

Mohamed Ridda LaouarAuthors Info & Claims

ICSENT 2018: Proceedings of the 7th International Conference on Software Engineering and New Technologies

Article No.: 17, Pages 1 - 5

https://doi.org/10.1145/3330089.3330117

Published: 26 December 2018 Publication History

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Abstract

Despite all the technological advancement in the field of power grids, there is still a need to enhance those grids, especially in case of extreme events as being the leading cause of continuous blackouts. The recent severe blackouts have highlighted the prominence of improving the resilience of the electric power grid. There has been a steep interesting in the last couple of years to this issue from the power industry and a number of researchers were motivated to diagnose the issue via attempting to suggest ways to improve the self-healing ability. Nevertheless, issues pertinent to validity and resiliency are still raised. This paper proposes an architecture for intelligent decision support system based on deep learning algorithms that can help operators to decide what to do against blackout. The system can offer decision support in the power restoration process. The system aim to restore power in a quick and effective manner in order to reduce blackout duration as well as the economic losses.

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Cited By

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Khediri ALaouar MEom S(2020)Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction ModelRecent Advances in Computer Science and Communications10.2174/221327591266619080911394513:3(508-518)Online publication date: 12-Aug-2020
https://doi.org/10.2174/2213275912666190809113945
Khediri ALaouar MEom S(2020)Decision Support System Architecture for Smart Grid’s Hardening Against Weather HazardAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36475-5_8(77-89)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-36475-5_8

Index Terms

Intelligent Decision Support System for Electric Power Restoration
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Hardware
  1. Power and energy
    1. Energy distribution
      1. Smart grid

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Published In

ICSENT 2018: Proceedings of the 7th International Conference on Software Engineering and New Technologies

December 2018

201 pages

ISBN:9781450361019

DOI:10.1145/3330089

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 December 2018

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ICSENT 2018

ICSENT 2018: 7th International Conference on Software Engineering and New Technologies

December 26 - 28, 2018

Hammamet, Tunisia

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Cited By

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Khediri ALaouar MEom S(2020)Enhancing Resiliency Feature in Smart Grids through a Deep Learning Based Prediction ModelRecent Advances in Computer Science and Communications10.2174/221327591266619080911394513:3(508-518)Online publication date: 12-Aug-2020
https://doi.org/10.2174/2213275912666190809113945
Khediri ALaouar MEom S(2020)Decision Support System Architecture for Smart Grid’s Hardening Against Weather HazardAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36475-5_8(77-89)Online publication date: 3-Jan-2020
https://doi.org/10.1007/978-3-030-36475-5_8

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Recommendations

Deep-Belief Network Based Prediction Model for Power Outage in Smart Grid

Reliability modeling for the advanced electric power grid

A stochastic negotiation approach to power restoration problems in a smart grid