Title :
Enhancement of power system data debugging using GSA-based data-mining technique
Author :
Huang, Shyh-Jier ; Lin, Jeu-Min
Author_Institution :
Dept. of Electr. Eng., Nat. Cheng Kung Univ., Tainan, Taiwan
fDate :
11/1/2002 12:00:00 AM
Abstract :
In this paper, a gap-statistic-algorithm (GSA)-based data-mining technique is applied to enhance the data debugging in power system operations. In the proposed approach, the GSA technique is embedded into a neural network frame in anticipation of improving the detection capability of bad data. Thanks to the clustering capability exhibited by GSA in which the number of clusters can be optimally determined, the proposed approach becomes highly effective to localize the group of abnormal data. This proposed approach has been tested through the data collected from different scenarios made on an IEEE 30-bus system and 118-bus systems. Test results reveal the feasibility of the method for the data diagnosis applications.
Keywords :
data mining; neural nets; power system analysis computing; statistical analysis; GSA-based data-mining technique; IEEE 118-bus system; IEEE 30-bus system; bad data detection; clustering capability; computer simulation; gap-statistic-algorithm; neural network; power system data debugging enhancement; Application software; Competitive intelligence; Computational intelligence; Data mining; Debugging; Neural networks; Power system reliability; Power system security; Power systems; Statistics;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2002.804992
