mlsanityData to 7 Oct 2026

Supporting Future Electrical Utilities: Using Deep Learning Methods in EMS and DMS Algorithms

arXiv 2303.00428 ↗First posted 1 Mar 2023cs.LG cs.AI cs.SYcited by 1

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Abstract (arXiv)

Electrical power systems are increasing in size, complexity, as well as dynamics due to the growing integration of renewable energy resources, which have sporadic power generation. This necessitates the development of near real-time power system algorithms, demanding lower computational complexity regarding the power system size. Considering the growing trend in the collection of historical measurement data and recent advances in the rapidly developing deep learning field, the main goal of this paper is to provide a review of recent deep learning-based power system monitoring and optimization algorithms. Electrical utilities can benefit from this review by re-implementing or enhancing the algorithms traditionally used in energy management systems (EMS) and distribution management systems (DMS).

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 41 references that are in the map. First posted on arXiv 1 Mar 2023.

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13 of 41 references.

References · 13

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1 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systemscited by 0