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Master’s in Probabilistic Analysis and Network Resilience Engineering

Online master's programme of 1,900 hours in probabilistic analysis and power network resilience engineering. 12 modules covering reliability and adequacy, Monte Carlo methods, extreme events and fragility curves, resilience metrics and planning, system restoration, cyber-physical resilience and asset risk management, 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

Apply probabilistic models to the analysis of power systems.

02

Calculate adequacy and reliability indices.

03

Assess the vulnerability of assets to extreme events.

04

Measure network resilience using quantitative metrics.

05

Plan resilience-oriented investments.

06

Design strategies for restoring supply.

07

Manage cyber-physical and asset risks.

Profile

Who it's for

  • Network reliability engineers.
  • Power system risk analysts.
  • Resilience planners at system operators and utilities.
  • Electrical asset managers.
  • Security of supply consultants.
  • Operation and restoration engineers.
  • Engineering or science graduates aiming to join a system operator.
Standards & tools

What you'll work with

  • Regulation (EU) 2019/943 (reliability standard and adequacy)
  • Regulation (EU) 2019/941 (risk-preparedness in the electricity sector)
  • Regulation (EU) 2017/2196 (emergency and restoration)
  • IEEE 1366 (distribution reliability indices)
  • Royal Decree 1955/2000 (quality of service)
  • ISO 55001:2014 (asset management systems)
  • Delegated Regulation (EU) 2024/1366 (network code on cybersecurity)
  • Directive (EU) 2022/2555 (NIS2) (cybersecurity of essential entities)
  • DIgSILENT PowerFactory
  • Python (NetworkX, pandapower)
  • MATLAB
  • QGIS
  • OpenDSS
Syllabus

12 modules · 1900 h

  1. 1.1Concepts of reliability, adequacy and security
  2. 1.2Failure rates and repair times
  3. 1.3Markov models of components
  4. 1.4Reliability of series and parallel systems
  5. 1.5Probability distributions in reliability
  6. 1.6Failure databases
  7. 1.7Supply reliability indices
  8. 1.8Value of lost load
  9. 1.9History of major blackouts
  10. 1.10Case study: failure statistics of a network
  1. 2.1Probabilistic load flow
  2. 2.2Sequential and non-sequential Monte Carlo simulation
  3. 2.3Analytical enumeration methods
  4. 2.4Variance reduction techniques
  5. 2.5Uncertainty models for renewables
  6. 2.6Spatial and temporal correlations
  7. 2.7Copulas for dependence
  8. 2.8Latin hypercube sampling
  9. 2.9Validation of probabilistic results
  10. 2.10Laboratory: probabilistic load flow with Monte Carlo
  1. 3.1LOLE, LOLP and EENS indices
  2. 3.2Generation capacity models
  3. 3.3Capacity outage probability tables
  4. 3.4Contribution of renewables to adequacy
  5. 3.5Storage and adequacy
  6. 3.6Interconnections in adequacy
  7. 3.7ENTSO-E ERAA methodology
  8. 3.8National reliability standard
  9. 3.9Cost of new entry
  10. 3.10Practical exercise: LOLE calculation for a test system
  1. 4.1SAIDI, SAIFI and CAIDI indices
  2. 4.2TIEPI and NIEPI indices in Spain
  3. 4.3Reliability analysis of radial networks
  4. 4.4Reliability of meshed networks
  5. 4.5Effect of automation on reliability
  6. 4.6Substation reliability
  7. 4.7Common-mode failures
  8. 4.8Protection failures
  9. 4.9Quality of service and regulation
  10. 4.10Laboratory: calculation of reliability indices for a feeder
  1. 5.1High-impact, low-probability events
  2. 5.2Storms, wind and ice
  3. 5.3Substation flooding
  4. 5.4Heatwaves and line capacity
  5. 5.5Wildfires and power lines
  6. 5.6Earthquakes and networks
  7. 5.7Geomagnetic storms and transformers
  8. 5.8Cyberattacks and physical attacks
  9. 5.9Future climate scenarios
  10. 5.10Case study: impact of a storm on a regional network
  1. 6.1Fragility curves for towers and lines
  2. 6.2Fragility of substation equipment
  3. 6.3Wind models on conductors
  4. 6.4Asset flooding models
  5. 6.5Vulnerability of underground networks
  6. 6.6Historical damage data
  7. 6.7Bayesian calibration methods
  8. 6.8Uncertainty in fragility
  9. 6.9Asset vulnerability maps
  10. 6.10Practical exercise: fragility curve of towers under wind loading
  1. 7.1The resilience triangle and trapezoid
  2. 7.2Robustness, recovery and adaptation metrics
  3. 7.3Operator resilience indicators
  4. 7.4Resilience of critical services
  5. 7.5Complex network analysis
  6. 7.6Centrality and topological vulnerability
  7. 7.7Cascading failures
  8. 7.8Propagation models
  9. 7.9Comparison of metrics
  10. 7.10Laboratory: topological vulnerability analysis with NetworkX
  1. 8.1Infrastructure hardening
  2. 8.2Selective undergrounding of lines
  3. 8.3Redundancy and meshing
  4. 8.4Microgrids as a resilience measure
  5. 8.5Distributed storage for resilience
  6. 8.6Cost-benefit analysis of measures
  7. 8.7Investment optimisation
  8. 8.8Planning under climate uncertainty
  9. 8.9Regulation of resilience investments
  10. 8.10Case study: resilience investment portfolio of a distribution system operator
  1. 9.1Preventive operation ahead of events
  2. 9.2Network reconfiguration
  3. 9.3Deployment of field crews
  4. 9.4Islanding
  5. 9.5System restoration plans
  6. 9.6Black start of power plants
  7. 9.7Prioritisation of critical loads
  8. 9.8Restoration with distributed resources
  9. 9.9Lessons from the 2025 Iberian blackout
  10. 9.10Practical exercise: restoration plan for an area after a blackout
  1. 10.1Interdependence between power and ICT
  2. 10.2Attacks on control systems
  3. 10.3The NIS2 Directive in the electricity sector
  4. 10.4Network code on cybersecurity
  5. 10.5Anomaly detection
  6. 10.6Response to cyber-physical incidents
  7. 10.7Crisis exercises
  8. 10.8Supply chain security
  9. 10.9Cyber-physical risk assessment
  10. 10.10Workshop: coordinated attack scenario on substations
  1. 11.1Asset management according to ISO 55000
  2. 11.2Asset health indices
  3. 11.3Risk-based maintenance
  4. 11.4Probability of failure and consequence
  5. 11.5Asset ageing
  6. 11.6Condition monitoring
  7. 11.7Prioritisation of renewals
  8. 11.8Management of strategic spares
  9. 11.9Risk reporting
  10. 11.10Case study: risk matrix for a transformer fleet
  1. 12.1Definition of the system and threats
  2. 12.2Probabilistic modelling of the system
  3. 12.3Reliability analysis
  4. 12.4Resilience assessment
  5. 12.5Proposal of measures
  6. 12.6Cost-benefit analysis of measures
  7. 12.7Implementation plan
  8. 12.8Conclusions and future work
  9. 12.9Master's Thesis report
  10. 12.10Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

The programme is delivered 100% online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so it can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is DIgSILENT PowerFactory, Python (NetworkX, pandapower), MATLAB, QGIS and OpenDSS, in free, academic or evaluation versions. Work is based on real procedures for Monte Carlo adequacy simulations, vulnerability analysis of networks under extreme events and cost-benefit assessment of resilience measures. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: Regulation (EU) 2019/943, Regulation (EU) 2019/941, Regulation (EU) 2017/2196, IEEE 1366, Royal Decree 1955/2000, ISO 55001:2014, Delegated Regulation (EU) 2024/1366, Directive (EU) 2022/2555 (NIS2).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2019/943, Regulation (EU) 2019/941 and Regulation (EU) 2017/2196 applied to real cases, not as a theoretical annex.
Industry software:DIgSILENT PowerFactory, Python (NetworkX, pandapower), MATLAB, QGIS and OpenDSS, in free, academic or evaluation versions.
Master's Thesis with defence:A complete resilience study of a power system, from threat definition and probabilistic modelling to cost-benefit analysis of measures, 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.

Model the system probabilistically
Run a Monte Carlo probabilistic load flow and calculate LOLE for the system.
Calculate reliability indices
Compute SAIDI, SAIFI and other reliability indices for a feeder.
Assess vulnerability
Build fragility curves and a topological vulnerability analysis with NetworkX.
Plan restoration
Draw up a restoration plan for an area after a blackout.
Master's Thesis
Present the full resilience study with proposed measures and cost-benefit analysis, defended before a panel.
Career opportunities

Where this takes you

Network reliability engineerPower system risk analystResilience plannerElectrical asset managerSecurity of supply consultantOperation and restoration engineer
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme 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.

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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 DIgSILENT PowerFactory, Python (NetworkX, pandapower), MATLAB, QGIS and OpenDSS, 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 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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