Digital twin course for electrical systems
About our
Digital Twins for Electrical Systems Course
This course introduces the concept of Digital Twins in the context of electrical engineering, covering their application in the design, simulation, optimization, and predictive maintenance of electrical systems. It explores tools and methodologies for creating virtual replicas of electrical systems, from distribution networks to renewable energy systems, enabling advanced analysis of performance, reliability, and security. Concepts such as electrical system modeling, transient analysis, and fault detection are integrated, with a focus on data-driven decision-making and system lifecycle optimization.
The course offers hands-on experience through the use of specialized simulation software, data analysis, and real-world case studies, addressing topics such as artificial intelligence and machine learning applied to asset management and the optimization of electrical system operation. The aim is to train professionals to improve energy efficiency, reduce maintenance costs, and prevent electrical system failures, while complying with current electrical safety regulations.
Digital twin course for electrical systems
- Format: Hybrid
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
725 $
Competencies and results
What you will learn
1. Digital Twin Domain for Electrical Systems: Design, Analysis and Optimization
- Advanced modeling and simulation of electrical systems using digital twins.
- Design of digital twins for the accurate representation of electrical components and systems.
- Analysis of the dynamic behavior of complex electrical systems in real time.
- Optimization of electrical systems through scenario simulation and identification of improvements.
- Implementation of predictive maintenance strategies based on digital twins.
- Use of digital twins for energy management and energy efficiency.
- Development of failure models and risk analysis in electrical systems.
- Integration of digital twins with SCADA systems and Internet of Things (IoT) platforms.
- Validation and verification of digital twins through real-world testing and measurements.
- Application of digital twins for the training and development of technical personnel.
2. Creation of Digital Twins: Simulation and Performance in Electrical Systems
- Fundamentals of Digital Twin Creation Applied to Electrical Systems.
- Principles of Simulation and Modeling of Electrical Components and Systems.
- Specialized Tools and Software for Digital Twin Creation.
- Optimizing the Performance of Electrical Systems Through Simulation.
- Integrating Real-Time Data for Digital Twin Updates.
- Failure Analysis and Lifespan Prediction in Electrical Systems.
- Practical Applications of Digital Twins in the Electrical Industry.
- Successful Case Studies in Digital Twin Implementation.
- Developing Analytical and Problem-Solving Skills.
- Future Trends in Digital Twin Technology for Electrical Systems.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Transformation of Electrical Systems with Digital Twins: Modeling, Simulation and Continuous Improvement
4. Transformation of Electrical Systems with Digital Twins: Modeling, Simulation, and Continuous Improvement
Understand the fundamentals of digital twins in electrical systems.
Model complex electrical systems using specialized software.
Simulate the behavior of electrical systems under various operating conditions.
Analyze simulation data to identify areas for improvement and optimization.
Implement digital twins for real-time monitoring of electrical systems.
Use digital twins for predictive analytics and proactive maintenance.
Explore the applications of digital twins in energy efficiency and sustainability.
Integrate digital twins with SCADA and asset management systems.
Develop continuous improvement strategies based on the results of digital twins.
Apply digital twins in the design and optimization of new electrical systems.
5. Advanced Simulation of Electrical Systems with Digital Twins
- You will master the analysis of complex electrical systems using digital twins, enabling a deep understanding of system behavior in real time.
- You will learn to simulate and analyze advanced electrical systems, including modeling critical components and their interactions.
- You will be trained in the use of specialized software for creating and simulating digital twins, mastering the necessary tools for predictive analysis.
- You will develop skills in identifying and diagnosing faults in electrical systems, using digital twins to simulate scenarios and predict outcomes.
- You will understand the application of digital twins in optimizing the performance and energy efficiency of electrical systems.
6. Digital Twins for Electrical Systems: Modeling, Simulation and Performance Optimization
- Understand the fundamentals of Digital Twins and their application in electrical systems.
- Model electrical components and systems using advanced simulation software.
- Simulate the behavior of electrical systems under different operating conditions.
- Optimize the performance of electrical systems through the analysis and simulation of Digital Twins.
- Analyze and mitigate energy efficiency problems in electrical systems.
- Develop predictive maintenance strategies based on Digital Twins.
- Implement Digital Twins for the management of electrical assets.
- Use Digital Twins for decision-making in the design and operation of electrical systems.
- Explore case studies and practical applications of Digital Twins in the electrical industry.
- Integrate Digital Twins with other technologies, such as the Internet of Things.
(IoT) and data analytics.
Who this program is for:
Digital twin course for electrical systems
- Graduates in Electrical Engineering, Electronics Engineering, Control Engineering, or related fields.
- Professionals from companies in the energy, transportation, infrastructure, and automation sectors.
- Engineers and technicians in electrical maintenance, operations, and design who wish to update their knowledge.
- Consultants, systems integrators, and research center personnel interested in the application of digital twins.
Recommended requirements: Basic knowledge of electrical systems and programming. Spanish/English level B2 or higher.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
1.1 Digital Twin Basics: Definition and Applications in Electrical Systems
1.2 Advantages of Digital Twins: Prediction, Optimization, and Predictive Maintenance
1.3 Digital Twin Architecture: Components and Structure
1.4 Electrical System Modeling: Types of Models and Tools
1.5 Real-Time Data Integration: Sensors and Communication
1.6 Simulation and Analysis: Tools and Methodologies
1.7 Practical Applications: Examples of Use in the Naval and Electrical Industries
1.8 Challenges and Opportunities: Implementation and Adoption
1.9 Case Studies: Success Stories and Lessons Learned
1.10 Introduction to Performance Optimization
2.2 Introduction to Creating Digital Twins in Electrical Systems
2.2 Fundamentals of Simulation in Electrical Systems
2.3 Selecting Tools and Software for Creating Digital Twins
2.4 Modeling Electrical Components: Motors, Generators, Transformers
2.5 Simulation of Electrical Circuits and Power Systems
2.6 Designing Simulation Scenarios: Faults, Overloads, Operating Conditions
2.7 Validation and Verification of Digital Twin Models
2.8 Performance Analysis and Optimization of Electrical Systems
2.9 Integrating Real-Time Data in Digital Twins
2.20 Practical Applications and Case Studies
3.3 Digital Twin Integration: From Conceptual Design to Implementation
3.2 Platforms and Tools for Creating Digital Twins in Electrical Systems
3.3 Implementing Digital Twins in Electrical Distribution Networks
3.4 Applying Digital Twins to Optimize Electrical Asset Management
3.5 Real-Time Monitoring and Fault Diagnosis with Digital Twins
3.6 Validating and Verifying Digital Twins in Real-World Environments
3.7 Integrating Data and Analyzing Digital Twins for Decision-Making
3.8 Evaluating Performance and Efficiency with Digital Twins
3.9 Case Studies: Successful Implementation of Digital Twins in Electrical Systems
3.30 Challenges and Future Considerations in Digital Twin Implementation
5.4 Modeling of Electrical Components: Motors, Generators, and Transformers
5.2 Simulation of Electrical Circuits: Tools and Methodologies
5.3 Transient and Steady-State Analysis of Electrical Systems
5.4 Implementation of Control Algorithms for Electrical Systems
5.5 Integration of Renewable Energy Sources in Digital Twins
5.6 Optimization of Energy Distribution in Electrical Systems
5.7 Fault Simulation and Protection Analysis in Electrical Systems
5.8 Design of Energy Management Systems (EMS) in Digital Twins
5.9 Validation and Verification of Digital Twin Models
5.40 Case Studies: Real-World Applications of Digital Twins in Electrical Systems
5.5 Introduction to Digital Twins in Electrical Systems
5.5 Fundamentals of Modeling for Digital Twins
5.3 Design of Electrical Components in Digital Environments
5.4 Creation and Integration of 3D Models
5.5 Modeling and Simulation Methodologies
5.6 Tools and Software for Digital Twin Design
5.7 Validation and Verification of Digital Models
5.8 Case Studies: Design of Electrical Systems with Digital Twins
5.5 Introduction to Simulation in Electrical Systems
5.5 Optimization of Electrical Systems with Digital Twins
5.3 Power Flow Analysis and Optimization
5.4 Simulation of Electrical Transients
5.5 Performance and Energy Efficiency Analysis
5.6 Simulation and Optimization Tools
5.7 Data-Driven Optimization of Digital Twins
5.8 Case Studies: Optimization of Electrical Systems
3.5 Implementation of Digital Twins in Real-World Systems
3.5 Real-Time Data Integration
3.3 Connecting Digital Twins with Sensors and Actuators
3.4 Developing User Interfaces for Evaluation
3.5 Remote Monitoring and Control of Electrical Systems
3.6 Real-Time Performance Evaluation
3.7 Design for Maintainability and Continuous Improvement
3.8 Case Studies: Implementation of Digital Twins
4.5 Advanced Modeling of Complex Electrical Systems
4.5 Simulation of Scenarios and Failures
4.3 Sensitivity Analysis and Risk Analysis
4.4 Continuous Improvement Based on Digital Twins
4.5 Feedback and Updating of the Digital Model
4.6 Implementation of Improvement Strategies
4.7 Design of Electrical Systems with Artificial Intelligence
4.8 Case Studies: Continuous Improvement in Electrical Systems
5.5 Simulation of Power Systems with Twins Digital Systems
5.5 Analysis of Complex Electrical Networks
5.3 Simulation of Renewable Energy Systems
5.4 Fault Simulation and System Protection
5.5 Advanced Simulation Tools
5.6 Results Analysis and Data Visualization
5.7 Design of Robust and Reliable Systems
5.8 Case Studies: Advanced Simulation
6.5 Optimization of Electrical System Performance
6.5 Modeling and Simulation of Control Systems
6.3 Power Quality Analysis
6.4 Energy Efficiency Optimization
6.5 Design of Advanced Control Strategies
6.6 Performance Evaluation in Different Scenarios
6.7 Applications of Digital Twins in Industry
6.8 Case Studies: Performance Optimization
7.5 Simulation Data Analysis
7.5 Interpretation of Results and Decision Making
7.3 Trend and Pattern Analysis
7.4 Identification of Bottlenecks and Opportunities
7.5 Optimization Based on Data Analysis
7.6 Use of Machine Learning Algorithms
7.7 Advanced Data Analysis Tools
7.8 Case Studies: Advanced Analysis
8.5 Rotor Design for Electric Motors
8.5 Rotor Modeling and Simulation
8.3 Rotor Design Optimization
8.4 Magnetic Flux Analysis
8.5 Dynamic Response Analysis
8.6 Design of Efficient and Reliable Rotors
8.7 Design of Rotors with Innovative Materials
8.8 Case Studies: Rotor Design and Analysis
6.6 Introduction to Performance Optimization in Electrical Systems
6.2 Modeling Electrical Components for Digital Twins
6.3 Simulation of Electrical Circuits with Digital Twins
6.4 Performance and Energy Efficiency Analysis
6.5 Optimization of Electrical System Design
6.6 Robustness and Reliability Management in Electrical Systems
6.7 Evaluation of the Impact of Environmental Factors
6.8 Case Studies: Optimization of Real Electrical Systems
6.9 Tools and Software for Optimization
6.60 Future Trends in Electrical System Optimization
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
Do you have any questions?
Our team is ready to help you. Contact us, and we will respond as soon as possible.
F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).