Deep Learning for 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 157
Special Issue Editors
Interests: nonlinear system identification and control; machine learning; networked control systems
Interests: nonlinear system identification; fault diagnosis; fault tolerant control; nonlinear control and nonlinear signal processing; nonlinear control; system identification; bio-inspired computing; machine intelligence; smart grid
Special Issue Information
Dear Colleagues,
The rapid addition of renewable energy sources, rising electricity demands, and the need for improved grid dependability have made it necessity to develop cutting-edge technologies for electrical power systems. The process of power transmission and distribution plays a key role in electrical power systems and has received less attention among researchers. Deep learning is an interesting possible solution which can be used for many applications in power systems.
This Special Issue aims to focus on deep-learning-based techniques to model and resolve issues related to power transmission and distribution. This Special Issue will accept topics regarding deep-learning-based applications in load forecasting, fault detection, and diagnosis; the assessment of the security and stability of power systems; the integration and management of renewable energy sources; and the asset management and maintenance of the electric grid. Other potential topics include:
- Deep networks for load forecasting;
- Deep networks for fault detection and diagnosis;
- Deep networks for the security and stability of power systems;
- Deep networks for the integration and management of renewable energy sources;
- Deep networks for asset management;
- Deep networks for the maintenance of the electric grid.
Dr. Chathura Wanigasekara
Dr. Akshya Swain
Guest Editors
Manuscript Submission Information
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Keywords
- power transmission
- power distribution
- deep learning
- smart grid
- renewable energy
- security and stability of power systems
- fault detection and diagnosis
