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- W2972433114 abstract "This paper proposes an improved gap statistics algorithm (GSA) to mine the data of the power system and complete the identification of bad data in the power system. The algorithm has been improved on the basis of neural network and cluster analysis. The original measurement data is preprocessed by RBF neural network, and the test results are clustered by k-means algorithm combined with the maximum and minimum distance. The elbow criterion obtains the best number of clusters. Through the clustering algorithm, bad data is separated from normal data to identify bad data in the power system. Compared with the traditional algorithm, it determines the number of clusters faster, shortens the running time, and improves the accuracy of identification. Finally, the data of IEEE33 node is used as the test data. The algorithm program is written by Matlab, and the simulation analysis is carried out by considering various bad data. The correctness and effectiveness of the algorithm are verified." @default.
- W2972433114 created "2019-09-19" @default.
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- W2972433114 date "2019-06-01" @default.
- W2972433114 modified "2023-09-26" @default.
- W2972433114 title "Bad data identification in power system based on Optimized GSA algorithm" @default.
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- W2972433114 doi "https://doi.org/10.1109/ccdc.2019.8833458" @default.
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