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Master'sEnergy and Networks Applications open

Master’s in Network and Asset Digital Twin Engineering

Online master's programme of 1,900 hours in digital twin engineering for grids and assets: 12 modules covering digital twin concepts, physical modelling of assets and networks, data integration, hybrid and machine learning models, generation and grid twins, visualisation, scenario simulation, platforms and cybersecurity, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Define the scope and maturity of a digital twin.

02

Model generation and grid assets physically.

03

Integrate real-time data into digital models.

04

Develop hybrid physics-based and machine learning models.

05

Build digital twins of power grids for operation.

06

Simulate scenarios to support investment decisions.

07

Ensure the security and governance of the digital twin.

Profile

Who it's for

  • Digital twin architects designing solutions for utilities and manufacturers.
  • Asset modelling engineers.
  • Energy data scientists.
  • Smart grid engineers.
  • Predictive maintenance specialists.
  • IoT platform developers.
  • Engineering or science graduates aiming to move into electricity transmission and distribution.
Standards & tools

What you'll work with

  • ISO 23247 (digital twin framework for manufacturing)
  • IEC 61970 (CIM, Common Information Model)
  • IEC 61968 (CIM for distribution management)
  • IEC 60076-7 (thermal ageing of power transformers)
  • ISO 55001:2014 (asset management)
  • IEC 61400-25 (communications for monitoring and control of wind power plants)
  • IEC 62443 (industrial automation and control systems security)
  • ISO 19650 (BIM information management)
  • Python (pandapower, PyTorch)
  • MATLAB/Simulink
  • Azure Digital Twins
  • Unity
  • DIgSILENT PowerFactory
Syllabus

12 modules · 1900 h

  1. 1.1Origins and definitions
  2. 1.2Digital twin maturity levels
  3. 1.3Digital twin, digital shadow and digital model
  4. 1.4Components: model, data and services
  5. 1.5Asset, system and process twins
  6. 1.6Applications in energy
  7. 1.7Standards and reference frameworks
  8. 1.8Benefits and costs
  9. 1.9Common mistakes in projects
  10. 1.10Case study: maturity assessment of an existing twin
  1. 2.1Thermal models of transformers
  2. 2.2Cable and line models
  3. 2.3Wind turbine models
  4. 2.4Photovoltaic plant models
  5. 2.5Battery models
  6. 2.6Circuit breaker models
  7. 2.7Finite element models
  8. 2.8Reduced-order models
  9. 2.9Validation of physical models
  10. 2.10Lab: thermal model of a transformer according to IEC 60076-7
  1. 3.1Electrical network model
  2. 3.2CIM and CGMES formats
  3. 3.3Distribution network models
  4. 3.4Steady-state simulation
  5. 3.5Dynamic network simulation
  6. 3.6Co-simulation models
  7. 3.7Automatic topology updating
  8. 3.8Integration with GIS
  9. 3.9Model version management
  10. 3.10Practical exercise: building a network model with pandapower
  1. 4.1Sensors and IoT
  2. 4.2SCADA and management systems
  3. 4.3IT/OT integration
  4. 4.4Data platforms
  5. 4.5Semantics and ontologies
  6. 4.6Data synchronisation
  7. 4.7Data quality
  8. 4.8Latency and update frequency
  9. 4.9Reference architectures
  10. 4.10Lab: twin connected to real-time MQTT data
  1. 5.1Grey-box models
  2. 5.2Calibration with data
  3. 5.3Physics-informed machine learning
  4. 5.4High-speed surrogate models
  5. 5.5Parameter estimation
  6. 5.6Data assimilation
  7. 5.7Kalman filters
  8. 5.8Uncertainty quantification
  9. 5.9Comparison of approaches
  10. 5.10Practical exercise: battery calibration with a Kalman filter
  1. 6.1Wind turbine twins
  2. 6.2Photovoltaic plant twins
  3. 6.3Hydroelectric plant twins
  4. 6.4Battery twins
  5. 6.5Remaining useful life prediction
  6. 6.6Performance optimisation
  7. 6.7Predictive maintenance with twins
  8. 6.8Manufacturer cases
  9. 6.9Return on investment
  10. 6.10Case study: wind farm twin for fatigue estimation
  1. 7.1Transmission twins
  2. 7.2Distribution twins
  3. 7.3Substation twins
  4. 7.4Contingency simulation
  5. 7.5Planning with twins
  6. 7.6Simulation-assisted operation
  7. 7.7Operator training
  8. 7.8Cases from European system operators
  9. 7.9Scaling to the whole grid
  10. 7.10Lab: twin of a feeder using smart meter data
  1. 8.13D visualisation of assets
  2. 8.2BIM and GIS integration
  3. 8.3Augmented reality in the field
  4. 8.4Virtual reality for training
  5. 8.5Digital twin dashboards
  6. 8.6Interface design
  7. 8.7Visualisation platforms
  8. 8.8Graphics performance of scenes
  9. 8.9User accessibility
  10. 8.10Practical exercise: 3D visualisation of a substation
  1. 9.1What-if scenario analysis
  2. 9.2Optimisation with twins
  3. 9.3Investment planning
  4. 9.4Risk management
  5. 9.5Climate scenarios for assets
  6. 9.6Maintenance simulation
  7. 9.7Decision support
  8. 9.8Validation of decisions
  9. 9.9Measuring benefits
  10. 9.10Case study: investment prioritisation with a grid twin
  1. 10.1Commercial digital twin platforms
  2. 10.2Open-source solutions
  3. 10.3Cloud architectures
  4. 10.4Edge computing
  5. 10.5Interoperability between platforms
  6. 10.6Platform scalability
  7. 10.7Platform security
  8. 10.8Operating costs
  9. 10.9Platform selection
  10. 10.10Workshop: reference architecture for a grid twin
  1. 11.1Security risks of the twin
  2. 11.2Protection of sensitive data
  3. 11.3IT/OT network segmentation
  4. 11.4Access control
  5. 11.5Digital twin governance
  6. 11.6Responsibilities and ownership
  7. 11.7Change management
  8. 11.8Digital twin life cycle
  9. 11.9Regulations applicable to the twin
  10. 11.10Practical exercise: security risk analysis of a twin
  1. 12.1Definition of the asset or network
  2. 12.2Physical modelling of the system
  3. 12.3Data integration
  4. 12.4Development of the hybrid model
  5. 12.5Digital twin services
  6. 12.6Validation with real data
  7. 12.7Value assessment
  8. 12.8Conclusions and future lines of work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable materials, recorded classes and an assigned technical tutor. There is no fixed timetable: you progress at your own pace within the intake, so it is compatible with full-time work.

Each module combines theory, guided reading of the original standards and applied exercises. The working software is Python (pandapower, PyTorch), MATLAB/Simulink, Azure Digital Twins, Unity and DIgSILENT PowerFactory, in free, academic or evaluation versions. Practical work is based on real procedures: thermal modelling of transformers, calibration of models with real data and grid scenario simulation. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ISO 23247, IEC 61970, IEC 61968, IEC 60076-7, ISO 55001:2014, IEC 61400-25, IEC 62443, ISO 19650.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ISO 23247, IEC 61970 and IEC 61968 applied to real cases, not as a theoretical annex.
Industry software:Python (pandapower, PyTorch), MATLAB/Simulink, Azure Digital Twins, Unity and DIgSILENT PowerFactory, in free, academic or evaluation versions.
Master's Thesis with defence:A digital twin of a real asset or network, validated with real data, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Scope and physical model
Define the asset or network and build its physical model, for example a transformer thermal model.
Real-time data integration
Connect the model to live data through IoT and MQTT.
Hybrid model calibration
Develop a hybrid model and calibrate it, for example with a Kalman filter.
Twin services and scenarios
Deliver twin services such as contingency or investment scenario simulation.
Master's Thesis
A digital twin validated with real data and a value assessment, defended before a panel.
Career opportunities

Where this takes you

Digital twin architectAsset modelling engineerEnergy data scientistSmart grid engineerPredictive maintenance specialistIoT platform developer
Admissions & scholarships

Requirements

  • A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • An internet connection and a computer capable of running the programme's software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

Apply now Scholarships
FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with Python (pandapower, PyTorch), MATLAB/Simulink, Azure Digital Twins, Unity and DIgSILENT PowerFactory, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the programme's software and English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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