Automatic Mapping Method for Power Supply Units in Medium-Voltage Distribution Networks Based on Generative Adversarial Network

doi:10.16183/j.cnki.jsjtu.2023.626

Abstract

Abstract:

With the gradual promotion of the “unit based” planning method for distribution networks, regional distribution networks have been divided into several relatively independent power supply units. However, there are multiple interconnecting lines within the power supply units, and the complexity of the structure makes the mapping of power supply units more difficult. The heuristic automated mapping methods based on rules and force orientation are inefficient and relies on manual intervention, which cannot adapt to the complex and changing distribution network application scenarios. Therefore, this paper proposes an automatic mapping method for medium-voltage distribution network power supply units based on the mean square error condition generation adversarial network. The layout generator and genetic mutation algorithm in this method can generate and optimize the layout and connection of distribution network nodes at a fine-grained level, achieving automatic mapping at various node scales. Then, it designs an evaluation function for node layout generators, which takes topology visualization performances such as node clustering degree, line crossing, and inflection points as key evaluation indicators. This function can be used to iteratively optimize the layout generator and thereby improve the mapping effect. The experimental results show that the method proposed outperforms other heuristic layout methods in terms of mapping effect.

Key words: medium-voltage distribution network, power supply unit, automatic mapping, generative adversarial network (GAN)

CLC Number:

TM715
TM732

CHEN Jinming, JIANG Wei, WANG Zhiwei, ZHU Zhenhan, CHEN Ye, ZHAO Yanchao. Automatic Mapping Method for Power Supply Units in Medium-Voltage Distribution Networks Based on Generative Adversarial Network[J]. Journal of Shanghai Jiao Tong University, 2025, 59(10): 1431-1441.

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模型	最低分数	平均分数
MLP	1.32	1.97
CGAN	1.43	2.07
MSE-CGAN	1.70	2.24
启发式 (Heuristic)	1.80	2.37
MSE-CGAN+GA	1.91	2.50

种群规模	交叉率	变异率	布局平均得分
100	0.5	0.01	2.29
100	0.5	0.05	2.32
100	0.5	0.10	2.31
100	0.6	0.05	2.36
100	0.6	0.10	2.34
500	0.5	0.05	2.42
500	0.5	0.10	2.41
500	0.6	0.05	2.50
500	0.6	0.10	2.47
500	0.7	0.05	2.49
800	0.7	0.10	2.51
1000	0.7	0.10	2.52