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Proceedings Paper

The application of genetic fuzzy clustering in bad data identification
Author(s): Yunjing Liu; Deying Gu
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Paper Abstract

Power system static state estimation is aimed at providing modern electric control centers with accurate and reliable real-time databases. To this end, not only should the state estimator be able to filter out random observation noise but it should also be able to detect the existence, identify the locations and remove the effects of bad data. Detecting and identifying bad data is very important in state estimation of power system. A new method presented in this paper is fuzzy clustering with genetic search. And simulation data proves that error contamination and submergence can be reduced so that real bad data can be detected and identified. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples. This method possesses characteristics so faster convergence rate and more exact clustering results than some typical clustering algorithms.

Paper Details

Date Published: 30 October 2006
PDF: 7 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63583G (30 October 2006); doi: 10.1117/12.718062
Show Author Affiliations
Yunjing Liu, Northeast Univ. at Qinhuangdao (China)
Deying Gu, Northeast Univ. at Qinhuangdao (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

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