基于数据增强及降维方法的配网业扩工程分类模型

doi:10.11930/j.issn.1004-9649.202204098

中国电力 ›› 2022, Vol. 55 ›› Issue (12): 91-97.DOI: 10.11930/j.issn.1004-9649.202204098

• 面向数字化转型的电力系统大数据分析技术 • 上一篇下一篇

基于数据增强及降维方法的配网业扩工程分类模型

周鑫¹, 林镜星¹, 谢志炜¹, 张铮², 梁濡铎², 欧祖宏²

1. 广东电网有限责任公司广州供电局，广东广州 510013;
2. 广东工业大学自动化学院，广东广州 510006

收稿日期:2022-04-21 修回日期:2022-11-02 发布日期:2022-12-28
作者简介:周鑫（1986—），男，硕士，高级工程师，从事配网工程管理研究，E-mail：286342697@qq.com;张铮（1997—），男，硕士研究生，通信作者，从事智能算法在电力系统中的应用研究，E-mail：294074267@qq.com
基金资助:
国家自然科学基金资助项目（61876040）；中国南方电网有限责任公司科技项目（080008KK52200010）

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

ZHOU Xin¹, LIN Jingxing¹, XIE Zhiwei¹, ZHANG Zheng², LIANG Ruduo², OU Zuhong²

1. Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou 510013, China;
2. College of Automation, Guangdong University of Technology, Guangzhou 510006, China

Received:2022-04-21 Revised:2022-11-02 Published:2022-12-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.61876040), Science and Technology Project of China Southern Power Grid Corporation (No.080008KK52200010).

摘要/Abstract

摘要： 配网业扩工程对供电企业开展电力供应工作具有重要意义。针对配电网业扩工程项目流程运转效率低下的问题，提出了一种基于数据增强和数据降维技术的配网业扩工程分类方法。该方法对原始数据进行数据增强，通过深度自编码器降低了数据维度，并进行特征提取及聚类分析。以某供电局配电网业扩工程项目数据为基础进行了仿真，实验结果表明：所用算法的分类准确性优于其他算法，所提方法能够合理分配业扩工程工期时长，实现对配网业扩工程的差异化管理，提高流程运转效率和客户满意度。

关键词: 配网业扩工程, 生成对抗网络, 深度自编码器, 鲸鱼优化算法

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

周鑫, 林镜星, 谢志炜, 张铮, 梁濡铎, 欧祖宏. 基于数据增强及降维方法的配网业扩工程分类模型[J]. 中国电力, 2022, 55(12): 91-97.

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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基于数据增强及降维方法的配网业扩工程分类模型

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

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基于数据增强及降维方法的配网业扩工程分类模型

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

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