A method for extracting correlative features of power grid big data based on improved deep learning

Xinyan Wang; Ying Zhu; Yongjie Ning; Jiacheng Du; Jingli Jia

doi:10.1117/12.2674980

23 May 2023 A method for extracting correlative features of power grid big data based on improved deep learning

Xinyan Wang, Ying Zhu, Yongjie Ning, Jiacheng Du, Jingli Jia

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260419 (2023) https://doi.org/10.1117/12.2674980
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

With the continuous maturity of big data, artificial intelligence, Internet of Things and other technologies, the rapid development of smart grid has been helped, but at the same time, the increasing line loss power has also attracted widespread attention. In the process of building a smart grid, each link of the grid operation generates a large number of multi-source heterogeneous data, including line loss data and line loss cause related data, which constitutes the big line loss data. First of all, considering the mining efficiency in big data, FP growth algorithm in association rule learning is selected to search the frequent item set of line loss features. Support, confidence and lift are used as evaluation indicators to analyze the association relationship between the causes of line loss; Secondly, a line loss prediction model based on deep learning is established. By eliminating the influence of line loss characteristics in turn, the correlation contribution of line loss causes to line loss is calculated to quantify the line loss caused by line loss causes. After verification, the depth confidence network and BP depth neural network as the prediction model of the depth learning method are superior to the shallow artificial neural network model in the prediction effect, and the prediction accuracy means the reliability of the contribution calculation. Finally, combined with the above two aspects of analysis, the causes of line loss in the substation area are comprehensively evaluated, and guidance suggestions are given to assist power enterprises in decision-making.

Citation Download Citation

Xinyan Wang, Ying Zhu, Yongjie Ning, Jiacheng Du, and Jingli Jia "A method for extracting correlative features of power grid big data based on improved deep learning", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260419 (23 May 2023); https://doi.org/10.1117/12.2674980

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KEYWORDS

Data modeling

Deep learning

Power grids

Education and training

Feature extraction

Mining

Reverse modeling

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