Abstract
This paper propose an optimized local outlier factor algorithm based on hierarchical clustering over grid bad measurement information, which affect the running safety of power grids phenomenon seriously. The method adopt statistical theory to evaluate the equipment running data and state information. Meanwhile, use clustering algorithm to analyze these data, to achieve the purpose of data reduction. While the relative entropy for data confirm the weight and thus enhance the accuracy of the algorithm. Experimental results show that the algorithm can quickly identify the bad power grid data.
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Qi, J., Cao, Y., Shi, J. (2017). Bad Data Identification Based on Optimized Local Outlier Detection Algorithm. In: Yuan, H., Geng, J., Bian, F. (eds) Geo-Spatial Knowledge and Intelligence. GRMSE 2016. Communications in Computer and Information Science, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-10-3966-9_22
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DOI: https://doi.org/10.1007/978-981-10-3966-9_22
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