Enhanced Power System Security Assessment Through Intelligent Decision Trees - Complex System Modelling and Control Through Intelligent Soft Computations - page 331

Enhanced Power System Security
Assessment Through Intelligent Decision
Trees

Venkat Krishnan

Abstract Power system security assessment involves ascertaining the post-contingency
security status based on the pre-contingency operating conditions. A system operator
accomplishes this by the knowledge of critical system attributes which are closely tied to
the system security limits. For instance, voltage levels, reactive power reserves, reactive
power

flows are some of the attributes that drive the voltage stability phenomena, and
hence provide easy guidelines for the operators to monitor and maneuver the highly
stressed power system to a secure state. With tremendous advancements in computational
power and machine learning techniques, there is increased ability to produce security
guidelines that are highly accurate and robust under a wide variety of system conditions.
Particularly, the decision trees, a data mining tool, has lend itself well in extracting highly
useful and succinct knowledge from a very large repository of historical information. The
most vital and sensitive part of such a decision tree based security assessment is the stage
of training database generation, a computationally intensive process which involves
sampling many system operating conditions and performing power system contingency
assessment simulations on them. The classification performance of operating guidelines
under realistic testing scenarios depend heavily on the quality of the training database
used to generate the decision trees. So the primary objective of this chapter is to develop
an improvised database generation process that creates a satisfactory training database by
sampling the most in

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uential operating conditions from the input operating parameter
state space prior to the stage of power system contingency simulation. Embedding such
intelligence to the system scenario sampling process enhances the information content in
the training database, while minimizing the computing requirements to generate it. This
chapter will clearly explain and demonstrate the process of identifying such high infor-
mation contained sampling space and the advantage of deriving security guidelines from
decision trees that exclusively use such an enhanced training database.

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Keywords Security assessment Operating guidelines Decision trees Intelligent

training set Monte Carlo simulation Importance sampling