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 CDGA course the delegates will:

• Participants will have practical knowledge to implement and manage AI-powered solutions in UPS systems.

• 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.

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 minimum of 80% of the total course duration.