Employing deep learning neural networks in mathematical basis of digital twins of electrical power systems

Development problems for digital twins of contemporary active power distribution systems are considered. Approaches to employing deep learning neural networks in digital twin-based intelligent control of these systems are highlighted. A brief review of relevant neural network architectures is outlined. Examples of neural network tools for solving a number of key intelligent control problems are presented, including load forecasting, electricity price forecasting, economic dispatch, power machine health assessment and prediction, and faults and disasters diagnosis. Recommendations are provided regarding alternative deployment modes of the presented neural network tools, such as inclusion into a digital twin basic mathematical software, or supply as auxiliary applications for certain categories of users.