A Distribution Network Expansion Project Classification Model Based on Data Augmentation and Dimensionality Reduction Method

doi:10.11930/j.issn.1004-9649.202204098

Abstract

Abstract: Distribution network expansion project is of great significance for power supply enterprises to carry out power supply work. Aiming at the low efficiency of the process operation of distribution network business expansion project, a classification method of distribution network business expansion project based on data enhancement and data dimension reduction technology is proposed. This method enhances the original data, reduces the data dimension through depth self encoder, and performs feature extraction and clustering analysis. Based on the data of a distribution network expansion project of a power supply bureau, the simulation results show that the classification accuracy of the algorithm used in this paper is better than other algorithms. The proposed method can reasonably allocate the duration of the industrial expansion project, realize the differential management of the distribution network industrial expansion project, and improve the process operation efficiency and customer satisfaction.

Key words: distribution network expansion project, generative adversarial network, deep auto-encoder, whale optimization algorithm

ZHOU Xin, LIN Jingxing, XIE Zhiwei, ZHANG Zheng, LIANG Ruduo, OU Zuhong. A Distribution Network Expansion Project Classification Model Based on Data Augmentation and Dimensionality Reduction Method[J]. Electric Power, 2022, 55(12): 91-97.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202204098

https://www.electricpower.com.cn/EN/Y2022/V55/I12/91

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