Power System Security Assessment - Application of Learning Algorithms

Abstract: 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