Deep Learning Applications in Optimization and Control of Power Transmission and Distribution

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 171

Special Issue Editors

E-Mail Website
Guest Editor
Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA
Interests: optimization, control and machine learning applied to complex networked systems; reliability, resilience and real-time control of energy systems

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA 92093, USA
Interests: statistical electrical load modeling and control of distributed energy resources in power systems

E-Mail Website
Guest Editor
Electrical Engineering, Colorado School of Mines, Golden, CO 80401, USA
Interests: power system; renewable integration; AI and machine learning; simulation; stability and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the growth in the amounts of data collected in power systems due to the introduction of higher-resolution sensors at various levels of the system and the push toward automation and intelligent data-driven decision making, deep learning-based AI methods have received significant interest in recent years. The ability of deep learning to consume large quantities of high-resolution data and develop high-fidelity accurate models can be used to make fast and efficient decisions applicable to all levels of power systems, from forecasting to planning to real-time decision making. However, several challenges remain, especially in relation to providing guarantees for the safe and reliable operation of the power system, as well as developing modeling that are explainable to network operators. Furthermore, different challenges in adopting deep learning exist at the transmission and distribution levels.

This Special Issue aims to develop explainable deep learning models for the safe operation of power systems at the transmission and distribution levels.

Dr. Nawaf Nazir
Dr. Adil Khurram
Dr. Qiuhua Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • deep learning
  • power transmission
  • power distribution
  • optimization and control of power systems

Published Papers

This special issue is now open for submission.
Back to TopTop