Enhancement of power system data debugging using GSA-based data-mining technique

Shyh Jier Huang; Jeu Min Lin

doi:10.1109/TPWRS.2002.804992

Enhancement of power system data debugging using GSA-based data-mining technique

Shyh Jier Huang, Jeu Min Lin

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

33 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1022-1029
Number of pages	8
Journal	IEEE Transactions on Power Systems
Volume	17
Issue number	4
DOIs	https://doi.org/10.1109/TPWRS.2002.804992
Publication status	Published - 2002 Nov

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering

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10.1109/TPWRS.2002.804992

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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.",

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Enhancement of power system data debugging using GSA-based data-mining technique

Abstract

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