Center for Artificial Intelligence and Machine Learning Applications in Electrical and Electronics Engineering

This Centre will be dedicated to advancing Artificial Intelligence (AI) and Machine Learning (ML) applications within the field of Electrical and Electronics Engineering. The centre will focus on leveraging AI/ML to solve complex challenges in power systems, control systems, smart grids, renewable energy integration, and signal processing. By fostering interdisciplinary research, collaboration with industry, and development of cutting-edge solutions, the CoE aims to position itself as a leader in AI/ML-driven electrical engineering innovation.

Electrical Engineering (EE) faces increasing complexity due to the rise of renewable energy, smart grid systems, and real-time monitoring requirements. Traditional methods often fall short in addressing these challenges efficiently. AI/ML technologies, with their ability to process large datasets, identify patterns, and optimize systems, provide transformative potential for the field.

The Center will serve as a hub for research, education, and innovation, focusing on the integration of AI/ML methodologies into EE domains such as Smart power Control, Power Electronics and Drives, Embedded Systems etc.,

1. Research and Development
   • Develop AI/ML models for predictive maintenance in power systems.
   • Design optimization algorithms for energy efficiency in smart grids and renewable energy systems.
   • Implement AI-driven fault detection and diagnosis in electrical equipment.
2. Education and Training
   • Train students and professionals in AI/ML applications specific to EE.
   • Host workshops, seminars, and conferences to disseminate knowledge.
3. Industry Collaboration
   • Partner with utilities, technology companies, and regulatory bodies.
   • Promote AI/ML-driven solutions for real-world EE challenges.
4. Innovation and Commercialization
   • Develop AI/ML-based products and tools for EE applications.
   • Support startups and entrepreneurs through incubation programs.

1. Power Systems
   • Load forecasting using ML techniques.
   • Stability analysis and voltage regulation with AI-driven controls.
   • Modern Power System integrated with Distributed Generators
2. Control Systems
   • Real-time optimization and adaptive control using reinforcement learning.
   • Intelligent automation for manufacturing and process industries.
3. Smart Grids and IOT
   • AI-based demand-response management and grid stability solutions.
   • ML-driven cyber-security mechanisms for smart grids.
4. Signal Processing
   • Enhanced noise reduction, signal classification, and pattern recognition.
   • AI/ML applications in biomedical and communication signal analysis