International Conference on Artificial Intelligence Techniques For Electrical Engineering Systems


April 8-9, 2022

Department of Electrical and Electronics Engineering

Seshadri Rao Gudlavalleru Engineering College(Autonomous), Gudlavalleru, Andhra Pradesh, India

International Conference on Artificial Intelligence Techniques for Electrical Engineering Systems (AITEES-2022) invites submission of novel, recent area of innovation and previously unpublished research work/idea in the field of modern applications of artificial intelligence techniques to Electrical Engineering Systems. The applications of artificial intelligence related to various fields of electrical engineering are mentioned in the conference tracks. The conference is meant to discuss the challenges and applications of latest evolutionary computing techniques, neural networks, fuzzy logic, machine learning and data analytics in the fields of power systems, power electronics, robotics, automation, instrumentation, control systems, mechatronics and photonics. It will provide a platform to the students, researchers, scientists, faculty members, professionals and practitioners to interact, present and get innovative ideas in the field of Electrical Engineering.As a part of AITEES-2022, many keynote sessions are planned to enhance the research and innovation skills of participants. Eminent professors from academic institutions and world renowned industrial experts from India and abroadwill deliver keynote sessions.

Springer will publish all accepted and presented papers as Book chapters. We will soon announce the book series on our website as the book proposal approval request is pending with the Springer. The paper must be 12-15 pages in length (including all text, figures, and references). The initial manuscript must be submitted in pdf format only. Use a proper tool to convert the resulting source into a pdf document that has only scalable fonts with all fonts embedded. The images embedded in the paper must not contain transparent pixels (i.e., an alpha- channel of a transparent color) since this could lead to problems when displaying or printing the pdf. The pdf manuscript must not have Adobe Document Protection or Document Security enabled. The final or cameraready paper to be submitted as per Springer requirements. All authors must sign a copyright agreementafter receiving the acceptance.

Paper submissions are invited in modern areas of application of Artificial intelligence techniques to electrical engineering systems typically the following but not limited to:


Track 1: Swarm Optimization & Evolutionary Computing Techniques
  • Swarm and Evolutionary techniques for power systems
  • Soft computing techniques for design of renewable energy systems
  • Electricity markets and power system economics
  • Applications of multi-objective optimization in power systems
  • Applications of meta-heuristic algorithms for power electronics
  • Evolutionary computing techniques for control systems
  • Intelligent load forecasting for modern distribution system
  • Demand side response and energy management using optimization algorithms
  • Intelligent planning of AC and DC micro grid systems
  • Design of smart resilient and energy storage systems using optimization techniques
Track 2: Neural Networks and Fuzzy Logic Applications
  • Fuzzy logic and neural networks applications for power systems
  • Unsupervised learning and supervised learning applications
  • Intelligent fault diagnosis and classification
  • Applications of neural networks and fuzzy logic for power electronics
  • Artificial intelligence models for system identification
  • Neuro-fuzzy control techniques for nonlinear systems
  • Artificial intelligence for energy management of electric vehicles
  • Intelligent predictive and adaptive control strategies
  • AI applications in mechatronic engineering
  • Intelligent robotics and autonomous systems
  • AI applications in instrumentation and measurement systems
  • AI techniques for photonics and photonic materials
  • Applications in intelligent industrial control systems
Track 3: Machine Learning Techniques and Big Data Analytics
  • Machine learning applications in power electronics
  • Machine learning in control systems
  • Machine learning applications in power systems
  • Emerging security and data privacy challenges for utilities
  • Machine learning applications for mitigating cyber security issues
  • Big-data application in power systems
  • Deep learning for power system data analysis
  • Big data analytics on phasor measurement units
  • IoT technologies for power distribution and utilization systems
  • Smart and micro-grids incorporated with ML and IoT
  • Machine learning for SCADA and load forecasting
  • ML applications for aero space engineering