Power System Security Assessment - Application of Learning Algorithms

by Andersson, Christian

Abstract (Summary)
The last years blackouts have indicated that the operation and control of power systems may need to be improved. Even if a lot of data was available, the operators at different control centers did not take the proper actions in time to prevent the blackouts. This depends partly on the reorganization of the control centers after the deregulation and partly on the lack of reliable decision support systems when the system is close to instability. Motivated by these facts, this thesis is focused on applying statistical learning algorithms for identifying critical states in power systems. Instead of using a model of the power system to estimate the state, measured variables are used as input data to the algorithm. The algorithm classifies secure from insecure states of the power system using the measured variables directly. The algorithm is trained beforehand with data from a model of the power system. The thesis uses two techniques, principal component analysis (PCA) and support vector machines (SVM), in order to classify whether the power system can withstand an (n
Bibliographical Information:


School:Lunds universitet

School Location:Sweden

Source Type:Master's Thesis



Date of Publication:01/01/2005

© 2009 All Rights Reserved.