TY - JOUR
T1 - Enhancement of power system data debugging using GSA-based data-mining technique
AU - Huang, Shyh Jier
AU - Lin, Jeu Min
PY - 2002/11
Y1 - 2002/11
N2 - 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.
AB - 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.
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U2 - 10.1109/TPWRS.2002.804992
DO - 10.1109/TPWRS.2002.804992
M3 - Article
AN - SCOPUS:0036872857
SN - 0885-8950
VL - 17
SP - 1022
EP - 1029
JO - IEEE Transactions on Power Systems
JF - IEEE Transactions on Power Systems
IS - 4
ER -