Abstract
With the rapid development of data acquisition system in power grid, the data fusion of power grid has become more and more mature. Aiming at the problem of data missing in power grid, this paper proposes an intelligent identification method of power grid missing data based on generation countermeasure network with multi-dimensional feature fitting. Firstly, based on the fluctuation cross-correlation analysis (FCCA), the correlation between missing data and multidimensional features is analyzed, and the feature data set with strong correlation is selected; Secondly, the kernel principal component analysis (KPCA) algorithm is used to map the feature data set into a low-dimensional vector. Finally, the improved generation countermeasure network (WGAN) is used to reconstruct and distinguish the low-dimensional vector, fill in the missing data of power grid and enhance the identification ability of missing data. The simulation results show that the proposed method has higher data filling accuracy than the traditional data filling method.
Project Supported by the science and technology project of Jiangsu Electric Power Co., Ltd. (J2021046).
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References
Hemanth, G.R., Charles Raja, S.: Proposing suitable data imputation methods by adopting a stage wise approach for various classes of smart meters missing data – practical approach. Expert Syst. Appl. 187, 115911 (2022)
Zhu, J., Xu, W.: Real-time data filling and automatic retrieval algorithm of road traffic based on deep-learning method. Symmetry 13(1), 1 (2020)
Deng, W., Guo, Y., Liu, J., Li, Y., Liu, D., Zhu, L.: A missing power data filling method based on improved random forest algorithm. Chin. J. Electr. Eng. 5(4), 33–39 (2019)
Huang, G.: Missing data filling method based on linear interpolation and lightgbm. J. Phys. Conf. Ser. 1754(1), 012187 (2021)
Kong, Z., Zhao, J., Wang, L., Zhang, J.: A new data filling approach based on probability analysis in incomplete soft sets. Expert Syst. Appl. 184, 115358 (2021)
Farzana, A., Samira, S., Bassant, S.: Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Comput. Sci. Rev. 40, 100378 (2021)
Zhang, C., Chen, H., He, J., Yang, H.: Reconstruction method for missing measurement data based on Wasserstein generative adversarial network. JACIII 25(2), 195–203 (2021)
Hua, W., Sui, Y., Wan, Y., Liu, G., Xu, G.: FCCA: hybrid code representation for functional clone detection using attention networks. IEEE Trans. Reliab. 70, 304–318 (2021)
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Lv, Y., Sun, S., Zhao, Q., Tian, J., Li, C. (2022). Research on Intelligent Identification Method of Power Grid Missing Data Based on Improved Generation Countermeasure Network with Multi-dimensional Feature Analysis. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_55
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DOI: https://doi.org/10.1007/978-981-19-0852-1_55
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