Guest Editors
Prof. Dr. M. Premkumar
Associate Professor, Department of Electrical and Electronics Engineering
Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078, India
Dr. Pradeep Jangir
Assistant Engineer, Power and Energy Systems
Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Sikar, Rajasthan 332025, India
Prof. Dr. C. Kumar
Professor, Department of Electrical and Electronics Engineering
M. Kumaraswamy College of Engineering, Karur, Tamil Nadu 639113, India
Summary
Artificial intelligence technology has made substantial advancements in modern power and renewable energy systems (RES), causing improvements in the current state of the art. Modern power and sustainable energy systems are being pushed to demonstrate more steady and good working results in efficacy, persistence, resilience and dependability, compact design, and intellectual ability due to the complex technological breakthroughs that are occurring. Electrical and RESs, on the other hand, are constantly confronted with technical problems and obstacles due to parametric and/or structural ambiguity, undesirable disruptions, faults, measurement noise, nonlinearities, equipment failure, and the limited online computational time available for control implementation and operation.
Numerous Evolutionary intelligence (EI) technologies, such as rule-based systems, artificial neural networks, fuzzy systems, Bayesian and statistical approaches, artificial immune systems, and hybrid systems combining evolutionary computation with other artificial intelligence techniques, have indeed been applied to RESs to address the concerns raised above and improve the overall performance of these systems. EI-based modeling, optimization, and control of renewable energy and power systems are the primary focus of this Special Issue, which includes examples of practical and industrial applications of recent advances, developments, and challenges in the field of EI.
The following are some examples of areas of interest for publication, but are not limited to:
l Artificial neural network and fuzzy logic technologies for modeling, optimization, and control of RESs
l EI-based fault detection of RESs
l EI-based sensor and actuators systems designed for RES
l Evolutionary and Swarm algorithms for modeling, optimization, and control of RESs
l EI-based risk and reliability assessment
l EI and IoT-based integrated frameworks for modeling, optimization, and control of RESs
l Reinforcement and Deep learning models for modeling, optimization, and control of RESs
l Energy trading and marketing
l Statistical and stochastical algorithms for modeling, optimization, and control of RESs, etc.
Keywords
Control of RES; Evolutionary Algorithms; Neural Network and Fuzzy Logic; Power system modeling and optimization; Renewable energy integration; Solar and Wind energy systems; Swarm-Based Algorithms
Published Papers
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