Prediction Method for 5G-enabled Smart Distribution Network Stability Based on Adaptive Gated Mixup Network

ZHANG Hao; LIU Dongla; WANG Rui; ZHANG Fangzhe

doi:10.20204/j.sp.2025.06004

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Prediction Method for 5G-enabled Smart Distribution Network Stability Based on Adaptive Gated Mixup Network

更新时间：2025-12-10

- Prediction Method for 5G-enabled Smart Distribution Network Stability Based on Adaptive Gated Mixup Network
- Vol. 53, Issue 6, Pages: 28-34(2025)
- 作者机构：
  
  1. 山东省智能电网技术创新中心,山东,济南,250003
  2. 山东省能源工业互联网大数据技术重点实验室,山东,济南,250003
- 作者简介：
- 基金信息：
- DOI：10.20204/j.sp.2025.06004
  CLC：
- Published：2025
- 稿件说明：
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张昊,刘冬兰,王睿,张方哲.基于自适应门控Mixup网络的5G智能配电网稳定性预测方法[J].智慧电力,2025,53(6):28-34.

ZHANG Hao, LIU Dongla, WANG Rui, et al. Prediction Method for 5G-enabled Smart Distribution Network Stability Based on Adaptive Gated Mixup Network[J]. 2025, 53(6): 28-34.
张昊,刘冬兰,王睿,张方哲.基于自适应门控Mixup网络的5G智能配电网稳定性预测方法[J].智慧电力,2025,53(6):28-34. DOI： 10.20204/j.sp.2025.06004.

ZHANG Hao, LIU Dongla, WANG Rui, et al. Prediction Method for 5G-enabled Smart Distribution Network Stability Based on Adaptive Gated Mixup Network[J]. 2025, 53(6): 28-34. DOI： 10.20204/j.sp.2025.06004.

摘要

随着智能配电网与5G通信技术的深度融合，电力系统在获得发展与创新的同时也面临着诸多挑战。针对配电网在复杂环境下稳定性预测准确率不高的问题，提出一种基于自适应门控Mixup网络（AGMN）的5G智能配电网稳定性预测方法。通过构建基于样本预测概率的自适应门控策略，并引入加权训练损失以增强模型的状态判别能力，从而改进虚拟样本的质量，最终实现预测模型准确率与泛化性能的全面提升。仿真分析表明，所提AGMN方法在不同样本类别分布下均表现出优异的预测效果和较强的泛化能力，且在样本类别不平衡场景中依然可以保持较高的准确率。

Abstract

With the deep integration of smart distribution networks and 5G communication technologies， power systems are encountering both developmental opportunities and numerous challenges. To address the issue of low accuracy in stability prediction for distribution networks under complex environments， this paper proposes a 5G-enabled smart distribution network stability prediction method based on an adaptive gated mixup network （AGMN）. By constructing an adaptive gated strategy grounded in sample prediction probabilities and introducing weighted training loss to enhance the model’s state discrimination capability， the quality of virtual samples is improved， thereby achieving comprehensive enhancement in prediction accuracy and generalization performance. Simulation analysis demonstrates that the proposed AGMN method exhibits superior prediction effectiveness and robust generalization capability under varying sample class distributions， while maintaining high accuracy even in class-imbalanced scenarios.

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