Dear Colleagues,

The need for new effective control approaches as alternatives to the classic control techniques is receiving a great deal of interest from the research community. One of the most promising control approaches is predictive control (PC), which has been extensively used in industrial applications (mainly chemical industries). The orientation of the PC approach towards electrical energy systems (i.e., power systems and machine drives) has seen a dramatic increase over the last two decades. The main features which distinguish PC from the traditional techniques are the simplicity of control configuration, the ability to handle system nonlinearity without incorporating modulators or linear controllers (i.e., PI controllers), and the ability to handle several control objectives at the same time. All of these merits motivate us as researchers to deepen the knowledge of this promising approach—analyzing its operation, modifying its configuration, and testing its performance for different applications. This Special Issue will publish original manuscripts presenting recent advances in the predictive control of electrical energy systems, with a special focus on topics including but not limited to the following:

• The application of predictive control to electrical power systems (i.e., frequency control, reliability, power quality).
• Utilization of predictive control to manage the operation of utility-scale converters (HVDC, solid-state transformers, FACTS, etc.).
• Utilization of predictive control in the integration process of renewable energy systems to utility grids.
• Application of predictive control in microgrids (AC, DC and hybrid).
• Predictive control for smart grids.
• Predictive control in autonomous systems.
• The application of predictive control to variable-speed electric machine drives and power electronic converters (e.g., DC/AC and multi-phase AC/AC converters).
• Novel formulations of predictive control for rotating machine drives (three-phase and multi-phase) and linear machine drives.
• Design of fault-tolerant predictive control algorithms for autonomous driving vehicles.
• Designing of predictive control algorithms for the post-fault operation of electric machine drives.
• Design and implementation challenges for predictive control: computation time, cost function formulation, weighting factor selection, parameter variation, delay compensation, variable switching frequency, etc.
• The application of predictive control in industrial applications such as electric vehicles, more electric aircrafts, naval ships, etc.

Dr. Mahmoud A. Mossa
Prof. Dr. Pierluigi Siano
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. Energies 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 2600 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.

• power system control
• predictive control algorithms
• autonomous systems
• smart grid control
• electric drives

Title: A Novel Predictive Control Scheme For a Linear Induction Motor Drive: Comparative Study with Classic Torque Control
Authors: Hamdi Echeikh
Affiliation: Electrical Engineering Department, National Engineering School of Monastir, 5035, Tunisia
Abstract: xx

Title: Comparative study between predictive torque control and predictive voltage control for a standalone wind driven Doubly Fed Induction Generator
Authors: Mahmoud Khalifa
Affiliation: Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt
Abstract: xx

Title: Adaptive Control Approach for Accurate Current Sharing and Voltage Regulation in DC Microgrid Applications
Authors: Mohamed Mesbah; Khairy Sayed; Adel Ahmed; Mahmoud Aref; Zakaria Elbarbary; Ali Saeed Almuflih; Mahmoud A. Mossa
Affiliation: Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 62523, Saudi Arabia
Abstract: A DC microgrid is an efficient way to combine diverse sources, conventional droop control is unable to achieve both accurate current sharing and required voltage regulation. This paper provides a new adaptive control approach for DC microgrid applications that satisfies both accurate current sharing and appropriate voltage regulation depending on the loading state. As the load increases, so do the output currents of the distributed generating units, and accurate current sharing is required under heavy load conditions. The suggested control approach raises the equivalent droop gains as the load level increases and provides accurate current sharing. In the adaptive droop controller, which is original and inventive, to do, the droop parameters have been checked online and changed using the principal current sharing loops to reduce the variation in load current sharing and the second loop also transferred the droop lines to eliminate DC microgrid bus voltage fluctuation. The proposed algorithm is tested using a variety of input voltages and load resistances. This work assesses the performance and stability of the suggested method using a linearized model and verifies the results using an acceptable model created in MATLAB/SIMULINK and using Real-Time Simulation Fundamentals and hardware-based experimentation.

Title: Enhancing the dynamic performance of an Electric Vehicle Using Novel Sensorless Predictive Control Scheme
Authors: Mohamed Reda
Affiliation: Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61111, Egypt
Abstract: xx