Research on Gas-Insulated Switchgear Partial Discharge Diagnosis and Location Method Based on Multi-Task Meta-Learning

WANG Yanxin; YAN Jing; GENG Yingsan; LIU Zhiyuan; WANG Jianhua

doi:10.7652/xjtuxb202407010

您当前的位置：

首页 >

文章列表页 >

Research on Gas-Insulated Switchgear Partial Discharge Diagnosis and Location Method Based on Multi-Task Meta-Learning

更新时间：2025-07-28

- Research on Gas-Insulated Switchgear Partial Discharge Diagnosis and Location Method Based on Multi-Task Meta-Learning
- Vol. 58, Issue 7, Pages: 105-115(2024)
- 作者机构：
  
  西安交通大学电工材料电气绝缘全国重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202407010
  CLC： TM85
- Online First：10 July 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
WANG Yanxin, YAN Jing, GENG Yingsan, et al. Research on Gas-Insulated Switchgear Partial Discharge Diagnosis and Location Method Based on Multi-Task Meta-Learning[J]. 2024, 58(7): 105-115.
DOI：

WANG Yanxin, YAN Jing, GENG Yingsan, et al. Research on Gas-Insulated Switchgear Partial Discharge Diagnosis and Location Method Based on Multi-Task Meta-Learning[J]. 2024, 58(7): 105-115. DOI： 10.7652/xjtuxb202407010.

摘要

针对气体绝缘组合电器(GIS)局部放电诊断和定位作为两个独立的任务来完成而忽略了两个任务间的联系、且难以部署和泛化到现场小样本场景下的问题

提出了多任务元学习网络以同时实现现场小样本场景下的GIS局部放电诊断与定位。首先

为了充分挖掘两个任务间的关联关系并保留每一任务的差异特征

构建了多任务网络

在特定任务层引入注意力机制

为每个任务从浅到深选择重要特征

保证每一任务特征差异化提取的质量。其次

为了将所开发的多任务网络部署应用到现场小样本场景下

采用元训练的方法对模型进行训练。再次

在元测试阶段使用目标任务的少量数据进行微调

实现了小样本下GIS局部放电诊断和定位。最后

在现场样本上对模型性能进行了验证。实验结果表明

提出的多任务元学习网络在GIS 局部放电诊断上的准确率达到94.53%

定位平均误差和均方根误差分别为10.78 cm和12.97 cm

与单任务网络和其他模型相比性能提升明显

为GIS 局部放电诊断和定位提供了新颖的解决思路。

Abstract

To address the issue where gas-insulated switchgear(GIS)partial discharge diagnosis and localization are treated as separate tasks without considering their connection and in response to challenging deployment and generalization to on-site small sample scenarios

this paper introduces a multi-task meta-learning network to simultaneously achieve GIS partial discharge diagnosis and localization in on-site small sample scenarios. Initially

a multi-task network is developed to fully explore the correlation between the two tasks while preserving their differential characteristics. An attention mechanism is introduced at specific task layers to select important features from shallow to deep levels for each task

ensuring the quality of differentiated extraction of features for each task. Subsequently

a meta-training method is adopted for model training to deploy the developed multi-task network in on-site small sample scenarios. Fine-tuning is performed using a small amount of data from the target task in the meta-testing stage

and GIS partial discharge diagnosis and location with small samples are enabled. Finally

the model performance is verified using field samples. Experimental results demonstrate that the multi-task meta-learning network proposed in this paper shows an accuracy of 94.53% in GIS partial discharge diagnosis

with an average error in the location of 10.78 cm and a root mean square error of 12.97 cm. Exhibiting superior performance relative to the single-task network and other models

the proposed multi-task meta-learning network presents a novel solution for GIS partial discharge diagnosis and location.

关键词

Keywords

references

宋颜峰, 郑中原, 于金山, 等. 机-电联合作用下SF6气体中金属微粒运动行为与局部放电特性研究 [J]. 西安交通大学学报, 2023, 57(7): 74-83.

SONG Yanfeng, ZHENG Zhongyuan, YU Jinshan, et al. Study on the motion behavior and partial discharge characteristics of metal particles in SF6 gas under the combined action of mechanical and electrical [J]. Journal of Xi'an Jiaotong University, 2023, 57(7): 74-83.

唐炬, 杨东, 曾福平, 等. 基于分解组分分析的SF6设备绝缘故障诊断方法与技术的研究现状 [J]. 电工技术学报, 2016, 31(20): 41-54.

TANG Ju, YANG Dong, ZENG Fuping, et al. Research status of SF6 insulation equipment fault diagnosis method and technology based on decomposed components analysis [J]. Transactions of China Electrotechnical Society, 2016, 31(20): 41-54.

KHAN Q, REFAAT S S, ABU-RUB H, et al. Partial discharge detection and diagnosis in gas insulated switchgear: state of the art [J]. IEEE Electrical Insulation Magazine, 2019, 35(4): 16-33.

王艳新, 闫静, 王建华, 等. 基于多级二阶注意力孪生网络的小样本GIS局部放电诊断方法 [J]. 电工技术学报, 2023, 38(8): 2255-2264.

WANG Yanxin, YAN Jing, WANG Jianhua, et al. Few-shot partial discharge diagnosis for gas-insulated switchgear using a novel multi-level second-order attention Siamese network [J]. Transactions of China Electrotechnical Society, 2023, 38(8): 2255-2264.

陈轩, 王立宪, 朱超, 等. 基于振动信号的气体绝缘金属封闭输电线路局部放电诊断方法 [J]. 电气技术, 2021, 22(12): 34-39.

CHEN Xuan, WANG Lixian, ZHU Chao, et al. Partial discharge diagnosis method of gas insulated transmission line based on vibration signal [J]. Electrical Engineering, 2021, 22(12): 34-39.

WANG Yanxin, YAN Jing, YANG Zhou, et al. Deep domain-invariant long short-term memory network for partial discharge localization in gas-insulated switchgear [J]. IEEE Transactions on Power Delivery, 2023, 38(4): 2810-2820.

WANG Yanxin, YAN Jing, YANG Zhou, et al. A domain adaptive deep transfer learning method for gas-insulated switchgear partial discharge diagnosis [J]. IEEE Transactions on Power Delivery, 2022, 37(4): 2514-2523.

张翼, 朱永利. 基于深度-广度联合剪枝的电力设备局部放电轻量化诊断方法 [J]. 电工技术学报, 2023, 38(7): 1935-1945.

ZHANG Yi, ZHU Yongli. A lightweight partial discharge diagnosis method of power equipment based on depth-width joint pruning [J]. Transactions of China Electrotechnical Society, 2023, 38(7): 1935-1945.

吴晨, 姚菁, 薛贵元, 等. 基于MMoE多任务学习和长短时记忆网络的综合能源系统负荷预测 [J]. 电力自动化设备, 2022, 42(7): 33-39.

WU Chen, YAO Jing, XUE Guiyuan, et al. Load forecasting of integrated energy system based on MMoE multi-task learning and LSTM [J]. Electric Power Automation Equipment, 2022, 42(7): 33-39.

ZHANG Yong, XIN Yuqi, LIU Zhiwei, et al. Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE [J]. Reliability Engineering System Safety, 2022, 220: 108263.

YAN Jianhai, HE Zhen, HE Shuguang. Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines [J]. Reliability Engineering System Safety, 2023, 234: 109141.

WANG Yanxin, YAN Jing, YANG Zhou, et al. GAN and CNN for imbalanced partial discharge pattern recognition in GIS [J]. High Voltage, 2022, 7(3): 452-460.

杨为, 朱太云, 张国宝, 等. 电力物联网下基于卷积神经网络和迁移学习的GIS局部放电模式识别分类方法研究 [J]. 高压电器, 2020, 56(9): 20-25.

YANG Wei, ZHU Taiyun, ZHANG Guobao, et al. Research on partial discharge pattern recognition and classification in GIS based on convolutional neural network and transfer learning in power internet of things [J]. High Voltage Apparatus, 2020, 56(9): 20-25.

刘颖, 雷研博, 范九伦, 等. 基于小样本学习的图像分类技术综述 [J]. 自动化学报, 2021, 47(2): 297-315.

LIU Ying, LEI Yanbo, FAN Jiulun, et al. Survey on image classification technology based on small sample learning [J]. Acta Automatica Sinica, 2021, 47(2): 297-315.

WANG Yanxin, YAN Jing, YANG Zhou, et al. Novel metric-based meta-learning model for few-shot diagnosis of partial discharge in a gas-insulated switchgear [J]. ISA Transactions, 2023, 134: 268-277.

王艳新, 闫静, 王建华, 等. 基于特征融合度量学习的高压断路器机械故障诊断 [J]. 仪器仪表学报, 2022, 43(9): 98-105.

WANG Yanxin, YAN Jing, WANG Jianhua, et al. Mechanical fault diagnosis for high voltage circuit breaker via a novel feature fusion metric learning [J]. Chinese Journal of Scientific Instrument, 2022, 43(9): 98-105.

ZHANG Shen, YE Fei, WANG Bingnan, et al. Few-shot bearing fault diagnosis based on model-agnostic meta-learning [J]. IEEE Transactions on Industry Applications, 2021, 57(5): 4754-4764.

邓旭晖, 陈中, 杨凯, 等. 基于多任务学习卷积网络的非侵入式负荷监测方法 [J]. 电力系统自动化, 2023, 47(8): 189-197.

DENG Xuhui, CHEN Zhong, YANG Kai, et al. Non-intrusive load monitoring method based on multi-task learning convolutional network [J]. Automation of Electric Power Systems, 2023, 47(8): 189-197.

XIE Zongliang, CHEN Jinglong, FENG Yong, et al. End to end multi-task learning with attention for multi-objective fault diagnosis under small sample [J]. Journal of Manufacturing Systems, 2022, 62: 301-316.

LI Yang, KAZEMEINI A, MEHTA Y, et al. Multitask learning for emotion and personality traits detection [J]. Neurocomputing, 2022, 493: 340-350.

王照伟, 刘传帅, 赵文祥, 等. 多尺度多任务注意力卷积神经网络滚动轴承故障诊断方法 [J/OL]. 电机与控制学报: 1-12[2023-10-22]. http://kns.cnki.net/kcms/detail/23.1408.tm.20230524.1025.018.html.WANG Zhaowei, LIU Chuanshuai, ZHAO Wenxiang, et al. Rolling bearing fault diagnosis with multi-scale multi-task attention convolutional neural network [J/OL]. Electric Machines and Control: 1-12[2023-10-22]. http://kns.cnki.net/kcms/deail/23.1408.tm.20230524. 1025.018.html.

BHUKYA A, KOLEY C. Bi-long short-term memory networks for radio frequency based arrival time detection of partial discharge signals [J]. IEEE Transactions on Power Delivery, 2022, 37(3): 2024-2031.

BOSMAN A S, ENGELBRECHT A, HELBIG M. Visualising basins of attraction for the cross-entropy and the squared error neural network loss functions [J]. Neurocomputing, 2020, 400: 113-136.

CHEN Jianjun, HU Weihao, CAO Di, et al. A meta-learning method for electric machine bearing fault diagnosis under varying working conditions with limited data [J]. IEEE Transactions on Industrial Informatics, 2023, 19(3): 2552-2564.

WANG Yanxin, YAN Jing, YANG Zhou, et al. A novel hybrid meta-learning for few-shot gas-insulated switchgear insulation defect diagnosis [J]. Expert Systems with Applications, 2023, 233: 120956.

王艳新, 闫静, 王建华, 等. 基于域对抗迁移卷积神经网络的小样本GIS绝缘缺陷智能诊断方法 [J]. 电工技术学报, 2022, 37(9): 2150-2160.

WANG Yanxin, YAN Jing, WANG Jianhua, et al. Intelligent diagnosis for GIS with small samples using a novel adversarial transfer learning in convolutional neural network [J]. Transactions of China Electrotechnical Society, 2022, 37(9): 2150-2160.

LIU Ruonan, YANG Boyuan, HAUPTMANN A G. Simultaneous bearing fault recognition and remaining useful life prediction using joint-loss convolutional neural network [J]. IEEE Transactions on Industrial Informatics, 2020, 16(1): 87-96.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Time-Domain Characteristic Site Analysis for Ultra-High Frequency Partial Discharge in Power Transformer

Measurement and Analysis for Pulse Current Shapes of Partial Discharge in Oil under DC Voltage

Propagation Characteristic of Partial Discharge Ultra High Frequency Signals Outside Transformer

Pulse Extraction for Partial Discharge with Double Channels with Independent Component Analysis

Fast and Practical Grouping Technique for Partial Discharge Pulse Sequence and Its Application

Related Author

高飞 2

吴锴 1

成永红 1

孟永鹏 1

赵煦 1

李军浩

司文荣

袁鹏

Related Institution

State Key Laboratory of Power Equipment and Electrical Insulation, Xi'an Jiaotong University

School of Electrical Engineering, Xi'an Jiaotong University

西安交通大学计算机科学与技术系

State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University

Electric Power Research Institute of Guangdong Power Grid Corpation

⁰