By Topic

Efficient Database Generation for Decision Tree Based Power System Security Assessment

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Venkat Krishnan ; Electr. & Comput. En gineering Dept., Iowa State Univ., Ames, IA, USA ; James D. McCalley ; Sebastien Henry ; Samir Issad

Decision tree based planning tools provide operators with the most important system attributes that guide them in deciding as to what situation requires operator action. Key to this approach is the manner in which different operating conditions are sampled to form a database for training. This paper develops an efficient sampling strategy that maximizes database information content while minimizing computing requirements. The approach involves two stages: stage-I to find the high information content region in the multidimensional operating parameter state space and stage-II to bias the sampling towards that region using importance sampling. The proposed approach is applied for deriving operating rules against voltage stability issues on the Brittany region of the French EHV system. The results show that the decision trees produced by the proposed efficient sampling approach have significantly improved classification performance and offer economic benefits compared to conventional sampling strategies, all at greatly reduced computational requirements.

Published in:

IEEE Transactions on Power Systems  (Volume:26 ,  Issue: 4 )