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Power System Security Assessment - Application of Learning Algorithms

Andersson, Christian LU (2005)
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... (More)
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 (Less)
Please use this url to cite or link to this publication:
author
supervisor
organization
publishing date
type
Thesis
publication status
published
subject
pages
82 pages
publisher
Department of Industrial Electrical Engineering and Automation, Lund Institute of Technology
ISBN
91-88934-39-X
language
English
LU publication?
yes
id
7421f44b-5a16-4df9-a0d6-af2626e36b15 (old id 587925)
alternative location
http://www.iea.lth.se/publications/Theses/LTH-IEA-1047.pdf
date added to LUP
2007-10-30 09:30:40
date last changed
2016-09-19 08:45:01
@misc{7421f44b-5a16-4df9-a0d6-af2626e36b15,
  abstract     = {The last years blackouts have indicated that the operation and control of<br/><br>
power systems may need to be improved. Even if a lot of data was available,<br/><br>
the operators at different control centers did not take the proper actions in<br/><br>
time to prevent the blackouts. This depends partly on the reorganization of<br/><br>
the control centers after the deregulation and partly on the lack of reliable decision<br/><br>
support systems when the system is close to instability. Motivated by<br/><br>
these facts, this thesis is focused on applying statistical learning algorithms<br/><br>
for identifying critical states in power systems. Instead of using a model of<br/><br>
the power system to estimate the state, measured variables are used as input<br/><br>
data to the algorithm. The algorithm classifies secure from insecure states<br/><br>
of the power system using the measured variables directly. The algorithm is<br/><br>
trained beforehand with data from a model of the power system.<br/><br>
The thesis uses two techniques, principal component analysis (PCA) and<br/><br>
support vector machines (SVM), in order to classify whether the power system<br/><br>
can withstand an (n},
  author       = {Andersson, Christian},
  isbn         = {91-88934-39-X},
  language     = {eng},
  pages        = {82},
  publisher    = {ARRAY(0xb039090)},
  title        = {Power System Security Assessment - Application of Learning Algorithms},
  year         = {2005},
}