The application of genetic fuzzy clustering in bad data identification

Yunjing Liu; Deying Gu

doi:10.1117/12.718062

30 October 2006 The application of genetic fuzzy clustering in bad data identification

Yunjing Liu, Deying Gu

Proceedings Volume 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation; 63583G (2006) https://doi.org/10.1117/12.718062
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

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.

Citation Download Citation

Yunjing Liu and Deying Gu "The application of genetic fuzzy clustering in bad data identification", Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63583G (30 October 2006); https://doi.org/10.1117/12.718062

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