This 5-Dayes, Instructor-Led, course aims to provide participants with a comprehensive understanding of how Artificial Intelligence (AI) can optimize UPS systems for improved reliability, efficiency, and resilience.

By the end of this course, participants will be able to:
This 5-Dayes, Instructor-Led, course aims to provide participants with a comprehensive understanding of how Artificial Intelligence (AI) can optimize UPS systems for improved reliability, efficiency, and resilience. By the end of the course, participants will have practical knowledge to implement and manage AI-powered solutions in UPS systems.

Who should attend:
• Electrical engineers.
• Facility managers.
• IT and data center professionals.
• UPS manufacturers and integrators.

Prerequisites:
• Basic knowledge of UPS systems.
• Familiarity with AI concepts (recommended but not mandatory).

Assessment:
• Pre-Test and Post-Test
• Daily quizzes.

• Lectures
• Workshop & Work Presentation
• Case Studies and Practical Exercise
• Videos and General Discussions

Module 1: Introduction to AI and UPS Systems
Chapter 1: Fundamentals of UPS Systems
• What is a UPS? Role and importance.
• Types of UPS: Offline, Online, and Line-Interactive.
• Components of a UPS: Rectifiers, Inverters, Batteries, and Controllers.
• Challenges in traditional UPS systems.

Chapter 2: Fundamentals of Artificial Intelligence
• Overview of AI: Definitions and Key Concepts.
• Types of AI: Narrow AI vs. General AI.
• AI Techniques: Machine Learning, Neural Networks, and Expert Systems.
• Applications of AI in various industries.

Chapter 3: Synergy Between AI and UPS
• Why AI for UPS? Addressing challenges.
• Benefits of integrating AI in UPS systems.
• Introduction to AI-enabled UPS use cases.

Module 2: AI for UPS Monitoring and Maintenance
Chapter 4: Real-Time Data Monitoring
• Sensors and IoT integration in UPS.
• AI algorithms for real-time monitoring.
• Dashboard and visualization tools.

Chapter 5: Predictive Maintenance
• Basics of predictive maintenance.
• Machine learning models for fault prediction.
• Case study: AI-driven battery health monitoring.

Chapter 6: Fault Detection and Diagnostics
• Common faults in UPS systems.
• AI for automated fault detection.
• Diagnostic tools and workflows.

Module 3: AI for Energy Optimization
Chapter 7: Load Management
• AI-driven load forecasting.
• Dynamic load balancing using AI.
• Case study: Reducing peak loads with AI.

Chapter 8: Efficiency Improvements
• AI algorithms for optimizing power conversion.
• Battery lifecycle management with AI.
• Case study: Energy savings in industrial UPS systems.

Chapter 9: Integration with Renewable Energy
• Challenges of renewable energy integration.
• AI for hybrid UPS systems with solar/wind energy.
• Real-world examples.

Module 4: Advanced AI Features in UPS Systems
Chapter 10: Cybersecurity in AI-Driven UPS
• Threats to UPS systems in a connected world.
• AI for intrusion detection and prevention.
• Secure communication protocols.

Chapter 11: Self-Learning and Adaptability
• Adaptive algorithms for UPS performance optimization.
• Examples of self-learning UPS systems.
• Case study: AI adapting to load changes in data centers.

Chapter 12: Remote Monitoring and Control
• Cloud-based UPS management platforms.
• AI for remote troubleshooting and updates.
• Best practices for remote UPS management.

Module 5: Implementation and Future Trends
Chapter 13: Implementation Strategies
• Steps to integrate AI into existing UPS systems.
• Hardware and software requirements.
• Vendor solutions and custom development.

Chapter 14: Regulatory and Compliance Considerations
• Standards for AI in power systems.
• Data privacy and security regulations.
• Environmental considerations.

Chapter 15: Future of AI in UPS
• Emerging AI technologies in power management.
• Role of AI in smart grids and microgrids.
• AI-driven autonomous power systems.

Chapter 16: Hands-On Workshop
• Setting up a simulation of an AI-enabled UPS system.
• Configuring and testing predictive maintenance algorithms.
• Developing a basic load forecasting model.

CDGA attendance certificate will be issued to all attendees completing a minimum of 75% of the total course duration