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 cycleThird cycle
Main field of study
Grading scaleP, 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
Control system reliability
Project work on statistical analysis of academic or real world problem.
Lectures (52h), project work (80h), presentations of project, exam.
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
· Oral presentation
· Project approved and delivered before deadline
Patrik Hilber, firstname.lastname@example.org
Patrik Hilber <email@example.com>
Course syllabus valid from: Autumn 2014.
Examination information valid from: Spring 2019.