Generalized Identification for Low-Pressure Abnormal Box-Table Relationship Based on Extreme Unbalance Classification Learning

GUAN Yong-ming; WANG Gang; LUO Kai-bo; LÜ Liang; LÜ Xiao-wen; SHI Yu-liang

doi:10.12263/DZXB.20191372

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Generalized Identification for Low-Pressure Abnormal Box-Table Relationship Based on Extreme Unbalance Classification Learning

PAPERS | 更新时间：2025-12-08

- Generalized Identification for Low-Pressure Abnormal Box-Table Relationship Based on Extreme Unbalance Classification Learning
- ACTA ELECTRONICA SINICA Vol. 49, Issue 8, Pages: 1507-1514(2021)
- 作者机构：
  
  1.山东大学软件学院，山东济南 250101
  2.山大地纬软件股份有限公司，山东济南 250100
  3.国网重庆市电力公司，重庆 400015
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20191372
  CLC： TP391;
- Received：13 December 2019，
  
  Revised：2020-10-09，
  
  Published：25 August 2021
- 稿件说明：
移动端阅览
管永明,王刚,骆凯波等.基于极端不平衡学习的泛化低压异常箱表关系识别研究与应用[J].电子学报,2021,49(08):1507-1514.

GUAN Yong-ming,WANG Gang,LUO Kai-bo,et al.Generalized Identification for Low-Pressure Abnormal Box-Table Relationship Based on Extreme Unbalance Classification Learning[J].ACTA ELECTRONICA SINICA,2021,49(08):1507-1514.
管永明,王刚,骆凯波等.基于极端不平衡学习的泛化低压异常箱表关系识别研究与应用[J].电子学报,2021,49(08):1507-1514. DOI： 10.12263/DZXB.20191372.

GUAN Yong-ming,WANG Gang,LUO Kai-bo,et al.Generalized Identification for Low-Pressure Abnormal Box-Table Relationship Based on Extreme Unbalance Classification Learning[J].ACTA ELECTRONICA SINICA,2021,49(08):1507-1514. DOI： 10.12263/DZXB.20191372.

摘要

针对低压配电网箱表关系存在人工核查成本高、异常案例少、难以实现异常规律捕获的问题，采用极端不平衡分类学习方法实现低压异常箱表关系识别的泛化应用推广.通过电压原理识别出部分异常箱表关系样本集，随后构建CNN（卷积神经网络）异常箱表关系识别模型，通过样本三分类赋权值实现类别均衡处理；并在模型推广应用过程中，采用强化学习实现离线模型的在线泛化学习，并以分组模型交互学习和竞争优化的方式筛选出最优泛化识别模型.实验证明，通过人工核查和数据反馈，该方法可实现模型对异常样本数据分布规律的自拟合学习，提高模型对不同应用环境的泛化性，进一步降低人工现场核查工作量，保障低压台区用户拓扑网络关系的准确性.

Abstract

Due to high labor cost and few abnormal cases of power box-table relations inspection

which difficulty to obtain the law. The extreme unbalanced classification learning method was used to capture the generalization. Through the principle of voltage

abnormal box-table relationship sample sets were identified. And by three-class weighting balance

the CNN(convolutional neural network) abnormal box-table relationship recognition model was constructed. In addition

the grouped parallel generalization learning of recognition model was realized by reinforcement learning. The experiment proves that

through self-learning the distribution of newly identified abnormal sample data

which improve the generalization to different environments. This reduces the workload of manual on-site verification and ensures the accuracy of the topology network relationship in the low-voltage station area.

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