Federated Learning-Based Insulator Fault Detection for Data Privacy Preserving

被引:2
作者
Luan, Zhirong [1 ]
Lai, Yujun [1 ]
Xu, Zhicong [1 ]
Gao, Yu [1 ]
Wang, Qian [1 ]
机构
[1] Xian Univ Technol, Sch Elect Engn, Xian 710048, Peoples R China
基金
中国国家自然科学基金;
关键词
vision sensor; insulator fault detection; federated learning; privacy-preserving;
D O I
10.3390/s23125624
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Insulators are widely used in distribution network transmission lines and serve as critical components of the distribution network. The detection of insulator faults is essential to ensure the safe and stable operation of the distribution network. Traditional insulator detection methods often rely on manual identification, which is time-consuming, labor-intensive, and inaccurate. The use of vision sensors for object detection is an efficient and accurate detection method that requires minimal human intervention. Currently, there is a considerable amount of research on the application of vision sensors for insulator fault recognition in object detection. However, centralized object detection requires uploading data collected from various substations through vision sensors to a computing center, which may raise data privacy concerns and increase uncertainty and operational risks in the distribution network. Therefore, this paper proposes a privacy-preserving insulator detection method based on federated learning. An insulator fault detection dataset is constructed, and Convolutional Neural Network (CNN) and Multi-Layer Perceptron (MLP) models are trained within the federated learning framework for insulator fault detection. Most of the existing insulator anomaly detection methods use a centralized model training method, which has the advantage of achieving a target detection accuracy of over 90%, but the disadvantage is that the training process is prone to privacy leakage and lacks privacy protection capability. Compared with the existing insulator target detection methods, the proposed method can also achieve an insulator anomaly detection accuracy of more than 90% and provide effective privacy protection. Through experiments, we demonstrate the applicability of the federated learning framework for insulator fault detection and its ability to protect data privacy while ensuring test accuracy.
引用
收藏
页数:20
相关论文
共 44 条
  • [31] Adversarial and Random Transformations for Robust Domain Adaptation and Generalization
    Xiao, Liang
    Xu, Jiaolong
    Zhao, Dawei
    Shang, Erke
    Zhu, Qi
    Dai, Bin
    [J]. SENSORS, 2023, 23 (11)
  • [32] Lightweight algorithm of insulator identification applicable to electric power engineering
    Xing, Zhiqiang
    Chen, Xi
    [J]. ENERGY REPORTS, 2022, 8 : 353 - 362
  • [33] Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification
    Yadav, Sanjay
    Shukla, Sanyam
    [J]. 2016 IEEE 6TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING (IACC), 2016, : 78 - 83
  • [34] Yakovlev A., 2020, Adapt. Autom. Steer. Syst, V1, P32, DOI 10.20535/1560-8956.36.2020.209755
  • [35] Yurochkin M, 2019, PR MACH LEARN RES, V97
  • [36] Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN
    Zaman, Wasim
    Ahmad, Zahoor
    Siddique, Muhammad Farooq
    Ullah, Niamat
    Kim, Jong-Myon
    [J]. SENSORS, 2023, 23 (11)
  • [37] Insulator Fault Detection Based on Spatial Morphological Features of Aerial Images
    Zhai, Yongjie
    Chen, Rui
    Yang, Qiang
    Li, Xiaoxia
    Zhao, Zhenbing
    [J]. IEEE ACCESS, 2018, 6 : 35316 - 35326
  • [38] Research on Rolling Bearing Fault Diagnosis Based on Digital Twin Data and Improved ConvNext
    Zhang, Chao
    Qin, Feifan
    Zhao, Wentao
    Li, Jianjun
    Liu, Tongtong
    [J]. SENSORS, 2023, 23 (11)
  • [39] A survey on federated learning
    Zhang, Chen
    Xie, Yu
    Bai, Hang
    Yu, Bin
    Li, Weihong
    Gao, Yuan
    [J]. KNOWLEDGE-BASED SYSTEMS, 2021, 216
  • [40] A Light-Weight Network for Small Insulator and Defect Detection Using UAV Imaging Based on Improved YOLOv5
    Zhang, Tong
    Zhang, Yinan
    Xin, Min
    Liao, Jiashe
    Xie, Qingfeng
    [J]. SENSORS, 2023, 23 (11)