EI3362 Power System Mathematical Statistics 8.0 credits

Matematisk statistisk för elkraftsystem

The course addresses mathematical statistical problems for power systems. Examples include application of probability density functions, lifetime modeling, sampling, and Monte Carlo techniques. The course runs in cooperation with the mathematical department at KTH and specific topics are determined every time the course is given.

  • Education cycle

    Third cycle
  • Main field of study

  • Grading scale

    P, F

Information for research students about course offerings

The course is given when there is sufficient demand. Please contact the examiner if you are interested in taking the course.

Intended learning outcomes

The course is aimed to you that need a statistical toolbox for power system analysis, with respect to design, operation and maintenance of the systems. After completed course the participants should have reached such level of knowledge that he/she can publish own work (peer-reviewed conference paper) on the topic of power system statistical analysis, within one or more of the following areas:

·         Statistical inference

·         Markov Chains

·         Game Theory

·         Convergence criterias in simulations

·         Statistical finance applications to power system modeling

·         Bayesian networks

·         Filtering techniques

·         Measurement techniques from a statistical viewpoint

Monte Carlo techniques

Course main content

Definitions and concepts in statistical theory for power systems

Network reliability

Component reliability

Control system reliability

Project work on statistical analysis of academic or real world problem.


Lectures (52h), project work (80h), presentations of project, exam.


PhD Student

Recommended prerequisites

Course in reliability analysis.


Depending on project assignment, one or more of:

·         Rausand Höyland: System Reliability Theory, 2nd ed.

·         J. R. Norris: Markov Chains, Cambridge Series in Statistical and Probabilistic Mathematics, Cambridge University Press.

·         T. Koski & J.Noble: Bayesian Networks and Causal Probability Calculus. 2009.Bayesian Networks: An Introduction (2009) published by Wiley.

·         Statistical Inference 2nd ed., G. Casella and R. Berger, Duxbury, 2002.

·         Theory of Statistics, M. Schervish, Springer, 1995.

·         Information Theory, Inference, and Learning Algorithms, D. Mackay, Cambridge University Press, 2003.


  • EXA1 - Examination, 8.0, grading scale: P, F

Requirements for final grade

·         Exam

·         Oral presentation

·         Project approved and delivered before deadline

Offered by

EECS/Electromagnetic Engineering


Patrik Hilber, hilber@kth.se


Patrik Hilber <hilber@kth.se>


Course syllabus valid from: Autumn 2014.
Examination information valid from: Spring 2019.