Dear Colleagues,

Nowadays, despite the already spent efforts in order to make the energy transition a reality, it is a matter of fact that power grids are not yet completely ready for such an ambitious scenario. Several factors still impair the suitability of actual electric power systems to fully handle the new upcoming scenario, which happens at different levels from power generation to power transmission and dispatch.

For instance, several problems arise from the actual high penetration of Renewable Energy Sources (RES) , characterized by a significant power generation variability. In addition to this, the presence of a large-scale integration of power electronic devices creates a reduction in the overall grid inertia, which induces, in turn, critical stability problems.

This Special Issue wishes to offer the opportunity to engineers, scientists, and experts to exchange state-of-the-art developments in the field of power systems analysis and control, with particular regard to the application of Machine Learning (ML) and Artificial Intelligence (AI) algorithms for stability/resiliency enhancement or predictive monitoring purposes.

The Special Issue aims to collect contributions targeted towards, but not limited to, the following main topics:

• Machine Learning and Artificial Intelligence algorithms for Power Systems Monitoring and Predictive Monitoring purposes;
• Power Systems Sub-Synchronous and Low-Frequency Oscillation (LFO) phenomena identification and mitigation using Machine Learning-based techniques and inter-area and local modes;
• Machine Learning methods enhancing the Transmission and Distribution Network infrastructure resilience, also with respect to cyber attacks;
• Renewable Energy Sources (RES) integration enhancement using Artificial Intelligence methods;
• Renewable energy system’s performances prediction using ML and AI;
• Short-Term Load Forecast (STLF) and renewable generation prediction by using Deep Learning (DL), Transfer Learning, and Reinforcement Learning (RL) methods;
• Machine Learning algorithms for power systems analysis and control;
• Severe weather conditions forecast using Artificial Intelligence;
• Model Predictive Control (MPC) of Power Systems with Machine Learning techniques;
• Dynamic rating improvement using Artificial Intelligence methods;

Dr. Carlo Olivieri
Dr. Mario Di Ferdinando
Dr. Yassine Chaibi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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.

• machine learning
• power systems
• predictive monitoring
• renewable energy sources