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A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation
Shen, Li1; Shen, Yang1; Song CH(宋纯贺)2; Li, Zhao1; Ran, Ran1; Zeng P(曾鹏)2
Department工业控制网络与系统研究室
Conference Name5th International Conference on Artificial Intelligence and Security, ICAIS 2019
Conference DateJuly 26-28, 2019
Conference PlaceNew York city, NY, United states
Source PublicationArtificial Intelligence and Security - 5th International Conference, ICAIS 2019, Proceedings
PublisherSpringer Verlag
Publication PlaceBerlin
2019
Pages499-508
Indexed ByEI
EI Accession number20193207287582
Contribution Rank2
ISSN0302-9743
ISBN978-3-030-24264-0
AbstractIdentification of anomaly data is very important for power system state estimation. In this paper, a method of power system anomaly data identification based on neural network and affine propagation is proposed. In this first step, a 3-layer neural network is trained as a predictor using normal data. In the second step, data to be detected is preprocessed using the trained neural network, and predicted residuals are obtained. In the third step, these predicted residuals are clustered using the affine propagation clustering algorithm, and in the final step, anomaly data is identified based on the clustering results. As the neural network training process is easy to fall into local minimum, which reduces the prediction accuracy of the neural network, in this paper a novel chaotic particle swarm optimization algorithm is proposed to train the neural network. From the experimental results it can be seen that, compared with previous anomaly data identification method using the BP neural network and the gap statistic algorithm or the K-mean clustering algorithm, the proposed method can effectively improve the accuracy of anomaly data identification.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/25393
Collection工业控制网络与系统研究室
Corresponding AuthorSong CH(宋纯贺)
Affiliation1.State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China;
2.Shenyang Institute of automation, Chinese Academy of Sciences, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
Recommended Citation
GB/T 7714
Shen, Li,Shen, Yang,Song CH,et al. A Novel Power System Anomaly Data Identification Method Based on Neural Network and Affine Propagation[C]. Berlin:Springer Verlag,2019:499-508.
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