Reference : Contingency severity assessment for voltage security using non-parametric regression ...
Scientific journals : Article
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/79721
Contingency severity assessment for voltage security using non-parametric regression techniques
English
Wehenkel, Louis mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Feb-1996
IEEE Transactions on Power Systems
IEEE
11
1
101-111
Yes (verified by ORBi)
International
0885-8950
Piscataway
NJ
[en] Voltage stability ; Machine Learning
[en] This paper proposes a novel approach to power system voltage security assessment exploiting nonparametric 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 offline 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 power system, where it shows to provide valuable physical insight and reliable contingency evaluation. Various potential uses in power system planning and operation are discussed
http://hdl.handle.net/2268/79721
10.1109/59.485991

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