Dear Colleagues,

Modern electrical and renewable energy systems are currently experiencing significant changes with the recent advances in artificial intelligence (AI) techniques and the standards of industry 4.0.

The complex technical changes are urging modern electrical and renewable energy systems to exhibit more stable and excellent operating performance in terms of effectiveness, persistence, robustness and reliability, design simplicity, and smartness.

However, electrical and renewable energy systems are continuously facing technical challenges and difficulties under parametric and/or structural uncertainties, undesired external disturbances, faults and trips, fast-varying references, sensor noises, nonlinearities, component failures, and the restricted online computing time of control execution.

In order to further address the above concerns and improve the overall performance of electrical and renewable energy systems, many computational intelligence (CI) technologies, such as fuzzy logic, neural networks, reinforcement learning, and evolutionary algorithms, have been utilized for modeling, control, estimation, and optimization of electrical and renewable energy systems. Meanwhile, the recent advancements in microcontrollers and digital signal processing technologies such as DSP and FPGA have facilitated real-time and in-the-loop implementation of CI-based methods for electrical and renewable energy systems.

The main goal of this Special Issue is to highlight the recent advancements, developments, and challenges in CI-based modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems with indications on practical and industry applications.

Topics of interest for publication include, but are not limited to, the following:

• Fuzzy logic techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• CI-based fault detection and prognostics of electrical motor/drive, renewable energy, and power systems
• Neural network techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• CI-based actuators and sensor/data fusion systems design for electrical motor/drive, renewable energy, and power systems
• Evolutionary algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• CI-based risk and reliability assessment of electrical motor/drive, renewable energy, and power systems
• Neuro-fuzzy techniques for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• CI-IoT-based integrated frameworks for control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• Deep learning and reinforcement learning for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems
• Stochastic learning and statistical algorithms for modeling, control, estimation, and optimization of electrical motor/drive, renewable energy, and power systems

Dr. Amirmehdi Yazdani
Dr. Amin Mahmoudi
Dr. GM Shafiullah
Dr. Irfan Ahmad Khan
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.

• fuzzy logic
• neural networks
• evolutionary algorithms
• deep and reinforcement learning

• Computational Intelligence-Based Modeling, Control, Estimation, and Optimization in Electrical Motor/Drive, Renewable Energy, and Power Systems in Energies (5 articles)