Contingency severity assessment for voltage security using<BR>non-parametric regression techniques
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Contingency severity assessment for voltage security using
non-parametric regression techniques
L. Wehenkel
Senior Research Assistant F.N.R.S. - Dept. of Electrical Engineering - Institut Montefiore
University of Liège - Sart Tilman B 28, B 4000 Liège - Belgium
Abstract:
This paper proposes a novel approach to voltage security assessment
exploiting non-parametric regression techniques to extract simple and
at the same time reliable models of the severity of a contingency,
defined as the difference between pre- and post-contingency load power
margins. The regression techniques extract information from large
sets of possible operating conditions of a power system screened
off-line via massive random sampling, whose voltage security with
respect to contingencies is pre-analyzed using an efficient voltage
stability simulation. In particular, regression trees are used to
identify the most salient parameters of the pre-contingency topology
and electrical state which influence the severity of a given
contingency, and to provide a first guess transparent approximation of
the contingency severity in terms of these latter parameters.
Multilayer perceptrons are exploited to further refine this
information. The approach is demonstrated on a realistic model of a
large scale voltage stability limited system, where it shows to
provide valuable physical insight and reliable contingency evaluation.
Various potential uses in power system planning and operation are
discussed.
Keywords - Voltage security; load power margin;
computer based learning; regression trees; artificial neural
networks.
Wed Jan 18 20:00:51 MET 1995