Introduction

The concept of a Digital Twin is rapidly transforming various industries, and its application in Power Systems is proving to be a game-changer for optimizing operations, enhancing reliability, and accelerating innovation. A Digital Twin is a virtual replica of a physical asset, system, or process, continuously updated with real-time data from sensors and other sources. This dynamic, living model allows for comprehensive monitoring, predictive analytics, performance optimization, and scenario simulation without impacting the actual physical system. For complex and geographically dispersed Power Systems, ranging from individual generation assets to entire transmission and distribution grids, Digital Twins offer unparalleled insights into system health, performance degradation, and potential failure points. Without leveraging the power of a Digital Twin for Power Systems, utilities and industrial operators risk reactive maintenance, suboptimal resource allocation, and a slower response to rapidly evolving grid dynamics and market demands, impeding their ability to build truly resilient and efficient energy infrastructures. This comprehensive training course focuses on equipping professionals with the expertise to master Digital Twin for Power Systems.

This training course is meticulously designed to empower electrical engineers, grid operators, data scientists, asset managers, IT professionals, and researchers with the theoretical understanding and practical skills necessary to conceptualize, develop, and deploy Digital Twins for Power Systems. Participants will gain a deep understanding of the core components of a Digital Twin, explore data acquisition and integration strategies, learn about advanced modeling and simulation techniques, and acquire skills in leveraging AI and machine learning for predictive insights. The course will delve into topics such as sensor integration (IoT), cloud computing platforms, real-time data analytics, fault detection and diagnostics, predictive maintenance scheduling, grid optimization, and the cyber-physical security considerations for digital twins. By mastering the principles and practical application of a Digital Twin for Power Systems, participants will be prepared to drive digital transformation initiatives, enhance operational efficiency, improve asset longevity, and contribute significantly to the development of smarter, more reliable, and sustainable power grids.

Duration: 5 Days

Target Audience

• Electrical Engineers (Utility, Consulting, Industrial)
• Power System Operators and Planners
• Data Scientists and Analysts
• Asset Management Professionals
• IT and Digital Transformation Specialists
• R&D Engineers in Energy Sector
• Control Systems Engineers
• Maintenance and Reliability Engineers
• Graduate Students in Power Systems
• Renewable Energy Developers

Objectives

• Understand the fundamental concepts and architecture of a Digital Twin.
• Learn about the key enabling technologies for Digital Twins in power systems (IoT, AI, Cloud).
• Acquire skills in identifying suitable use cases for Digital Twin implementation in power systems.
• Comprehend techniques for data acquisition, integration, and management from power system assets.
• Explore strategies for building accurate physics-based and data-driven models for Digital Twins.
• Understand the importance of real-time analytics and predictive capabilities in Digital Twins.
• Gain insights into applying Digital Twins for asset performance optimization and predictive maintenance.
• Develop a practical understanding of cybersecurity and data governance for Digital Twin deployments.

Course Content

Module 1: Introduction to Digital Twin Technology

• Definition and evolution of the Digital Twin concept.
• Key components of a Digital Twin: physical asset, virtual model, data link, insights.
• Digital Twin vs. simulation, modeling, and digital shadow.
• Benefits of Digital Twins across industries: efficiency, reliability, innovation.
• Overview of Digital Twin maturity levels.

Module 2: Digital Twin Architecture for Power Systems

• Data sources for power system Digital Twins: SCADA, sensors, smart meters, historical data.
• Internet of Things (IoT) sensors and connectivity for power assets.
• Cloud computing platforms and edge computing for data processing.
• Data ingestion pipelines and real-time data streaming.
• Architectural layers: physical layer, connectivity layer, platform layer, application layer.

Module 3: Modeling and Simulation for Power System Digital Twins

• Physics-based modeling of power system components (generators, transformers, lines, loads).
• Data-driven modeling using machine learning techniques.
• Hybrid modeling approaches combining physics and data.
• Real-time simulation of power system dynamics and transients.
• Model calibration and validation using real-world data.

Module 4: Data Management and Analytics for Digital Twins

• Data types in power systems: time-series, alarms, events, operational data.
• Data quality, cleansing, and pre-processing techniques.
• Big Data storage and processing technologies.
• Real-time analytics and anomaly detection.
• Data visualization and dashboarding for actionable insights.

Module 5: Digital Twin Applications in Power Generation

• Digital Twin for thermal power plants: boiler, turbine, generator optimization.
• Digital Twin for renewable energy: wind turbine performance, solar farm output prediction.
• Predictive maintenance for critical generation assets.
• Fuel consumption optimization and emissions reduction.
• Remote monitoring and diagnostics for power plants.

Module 6: Digital Twin Applications in Transmission and Distribution

• Digital Twin for substation assets: transformers, circuit breakers, switchgear.
• Grid network digital twin for real-time operational awareness.
• Load forecasting and demand-side management using Digital Twins.
• Fault detection, localization, and restoration optimization.
• Asset health monitoring and remaining life prediction for T&D infrastructure.

Module 7: AI, Machine Learning, and Predictive Maintenance

• Leveraging AI/ML algorithms for predictive analytics in Digital Twins.
• Machine learning models for fault prediction and condition assessment.
• Anomaly detection algorithms for early warning of equipment degradation.
• Optimized maintenance scheduling based on Digital Twin insights.
• Prescriptive analytics: recommending optimal actions.

Module 8: Implementation Challenges, Cybersecurity, and Future Trends

• Challenges in Digital Twin implementation: data integration, cost, organizational change.
• Cybersecurity threats to Digital Twins and mitigation strategies.
• Data privacy and governance considerations.
• Future trends: blockchain for data integrity, autonomous power systems, digital twins of microgrids.
• Case studies and best practices from leading utilities and energy companies.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.