Prerequisites: 

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

Assessment: 

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

By the end of this 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

• 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. 

BTS attendance certificate will be issued to all attendees completing minimum of 80% of the total course duration.