基于多维度信息融合的实用型变压器故障诊断专家系统

doi:10.11930/j.issn.1004-9649.2017.01.085.07

中国电力 ›› 2017, Vol. 50 ›› Issue (1): 85-91.DOI: 10.11930/j.issn.1004-9649.2017.01.085.07

基于多维度信息融合的实用型变压器故障诊断专家系统

徐阳¹, 谢天喜¹, 周志成¹, 陆云才¹, 李建生¹, 曹健², 蒋超³

1. 国网江苏省电力公司电力科学研究院,江苏南京 211103;
2. 国网江苏省电力公司检修分公司无锡分部,江苏无锡 214101;
3. 江苏方天电力技术有限公司,江苏南京 211106

收稿日期:2016-10-21 出版日期:2017-01-20 发布日期:2017-01-23
作者简介:徐阳（1987-）,男,江苏扬州人,博士,工程师,从事电力设备故障诊断和直流输电等方面的工作。E-mail: 15895838115@163.com
基金资助:
国家自然科学基金资助项目（51407026）

Practical Expert System for Transformer Fault Diagnosis Based on Multi-Dimensional Information Fusion Technology

XU Yang¹, XIE Tianxi¹, ZHOU Zhicheng¹, LU Yuncai¹, LI Jiansheng¹, CAO Jian², JIANG Chao³

1. State Grid Jiangsu Electric Power Company Research Institute, Nanjing 211103, China;
2. State Grid Jiangsu Electric Power Company Maintenance Branch Wuxi Branch, Wuxi 214101, China;
3. Jiangsu Fangtian Electronic Technology Corporation, Nanjing 211106, China

Received:2016-10-21 Online:2017-01-20 Published:2017-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (No. 51407026).

摘要/Abstract

摘要： 针对由于变压器结构复杂而导致其故障诊断的快速准确判断越来越依赖于大量信息的融合处理的情况,建立了基于多维度信息融合的实用型电力变压器故障诊断专家系统。首先分析多维度信息融合模型结构,给出基于因子分析计算各维度信息融合权重的方法,再构建基于多维度信息融合的专家系统,分析该专家系统的诊断流程,对多维度信息的识别、选择和判断进行了研究,最后结合110 kV变压器案例进行验证。结果表明,该专家系统逻辑简单、诊断精度高、诊断快速,可嵌入智能终端装置,为现场工作人员提供智能专家诊断。

关键词: 多维度信息融合, 专家系统, 电力变压器, 故障诊断, 因子分析

Abstract: Due to the complex structure of the power transformer, the fault diagnosis is more and more dependent on the fusion of a large amount of information. Therefore, a practical expert system for power transformer fault diagnosis based on multi-dimensional information fusion technology is proposed in this paper. First, the structure of the multi-dimensional information fusion model is analyzed, and then the method of factor analysis is proposed to calculate the information fusion weights. Based on the fusion technology, the practical expert system for transformer fault diagnosis is built, which is finally verified by an example of 110 kV main transformer. Moreover, the recognition, selection and excavation of the multidimensional data are analyzed. With the advantage of simple logic, high reliability, and fast diagnosis, the proposed expert system can be embedded in intelligent terminals to provide intelligent expert diagnosis for the staff on-site.

Key words: multi-dimensional information fusion, expert system, power transformer, fault diagnosis, factor analysis

中图分类号:

TM41

徐阳, 谢天喜, 周志成, 陆云才, 李建生, 曹健, 蒋超. 基于多维度信息融合的实用型变压器故障诊断专家系统[J]. 中国电力, 2017, 50(1): 85-91.

XU Yang, XIE Tianxi, ZHOU Zhicheng, LU Yuncai, LI Jiansheng, CAO Jian, JIANG Chao. Practical Expert System for Transformer Fault Diagnosis Based on Multi-Dimensional Information Fusion Technology[J]. Electric Power, 2017, 50(1): 85-91.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.2017.01.085.07

https://www.electricpower.com.cn/CN/Y2017/V50/I1/85

参考文献

[1] 万达,王建明,吴益明. 变压器的故障诊断与检修策略（一）[J].江苏电机工程,2003,22（5）：10-16.
WAN Da, WANG Jianming, WU Yiming. Faults diagnosis and maintenance strategy of power transformer(I)[J]. Jiangsu Electrical Engineering, 2003, 22(5): 10-16.
[2] 万达,王建明,吴益明. 变压器的故障诊断与检修策略（八）[J].江苏电机工程,2004,23（6）：12-14.
WAN Da, WANG Jianming, WU Yiming. Faults diagnosis and maintenance strategy of power transformer(Ⅷ)[J]. Jiangsu Electrical Engineering, 2004, 23(6): 12-14.
[3] 电力设备预防试验规程：DL/T 596-1996[S]. 北京：中国电力出版社,1996.
Preventive test code forelectric power equipment: DL/T 596-1996[S]. Beijing: China Electric Power Press, 1996.
[4] 变压器油中溶解气体分析和判断导则：DL/T 722-2014[S]. 北京：中国电力出版社,2014.
Guide to the analysis and the diagnosis of gases dissolved in transformer oil: DL/T 722-2014[S]. Beijing: China Electric Power Press, 2014.
[5] 张卫华,苑津莎,张铁峰,等. 应用B样条理论改进的变压器三比值故障诊断方法[J]. 中国电机工程学报,2014,34（24）：4129-4136.
ZHANG Weihua, YUAN Jinsha, ZHANG Tiefeng, et al . An improved three-ratio method for transformer fault diagnosis using B-spline theory[J]. Proceedings of the CSEE, 2014, 34(24): 4129-4136.
[6] 杨兵,丁辉,罗为民,等. 基于知识库的变压器故障诊断专家系统[J]. 中国电机工程学报,2002,22（10）：121-124.
YANG Bing, DING Hui, LUO Weimin, et al . Expert system of transformer fault diagnosis based on knowledge base[J]. Proceedings of the CSEE, 2002, 22(10): 121-124.
[7] 杜剑光. 变压器故障诊断专家系统[D]. 北京：华北电力大学（北京）,2003.
DU Jianguang. Transformer fault diagnosis expert system[D]. Beijing: North China Electric Power University, 2003.
[8] PURKAIT P, CHAKRAVORTI S. Time and frequency domain analyses based expert system for impulse fault diagnosis in transformers[J]. IEEE Transactions on Dielectrics & Electrical Insulation, 2002, 9(3): 433-445.
[9] BOCZAR T, CICHO A, BORUCKI S. Diagnostic expert system of transformer insulation systems using the acoustic emission method[J]. IEEE Transactions on Dielectrics & Electrical Insulation, 2014, 21(2): 854-865.
[10] 郑蕊蕊,赵继印,赵婷婷,等. 基于遗传支持向量机和灰色人工免疫算法的电力变压器故障诊断[J]. 中国电机工程学报,2011,31（7）：56-63.
ZHENG Ruirui, ZHAO Jiyin, ZHAO Tingting, et al . Power transformer fault diagnosis based on genetic support vector machine and gray artificial immune algorithm[J]. Proceedings of the CSEE, 2011, 31(7): 56-63.
[11] 王峰,王春宁. 基于辨识模型的变压器故障诊断方法研究[J].江苏电机工程,2014,33（1）：26-29.
WANG Feng, WANG Chunning. Research on transformer fault detection based on identification model[J]. Jiangsu Electrical Engineering, 2014, 33(1): 26-29.
[12] 胡青. 基于电力变压器故障特征气体分层特性的诊断与预测方法研究[D]. 重庆：重庆大学,2010.
HU Qing. Study on power transformer fault diagnosis and prediction based on layered characteristics of fault feature gases[D]. Chongqing: Chongqing University, 2010.
[13] 阮羚,谢齐家,高胜友,等. 人工神经网络和信息融合技术在变压器状态评估中的应用[J].高电压技术,2014,40（3）：822-828.
RUAN Ling, XIE Qijia, GAO Shengyou, et al . Application of artificial neural network and information fusion technology in power transformer condition assessment[J]. High Voltage Engineering, 2014, 40(3): 822-828.
[14] 邓宇,罗安,夏向阳. 变压器信息融合故障诊断技术[J]. 电工技术,2002（12）：5-6.
DENG Yu, LUO An, XIA Xiangyang. The technology of fault diagnosing for transformer information mixture[J]. Electric Engineering, 2002(12): 5-6.
[15] 曾丹乐.基于多源信息融合的变压器状态评估关键技术研究[D]. 上海：上海交通大学,2015.
ZENG Danle. Condition comprehensive assessment of power transformers based on online monitoring and software development[D]. Shanghai: Shanghai Jiao Tong University, 2015.
[16] 胡雁翔. 多源信息融合技术在变压器故障诊断中的研究[D]. 北京：华北电力大学（北京）,2013.
HU Yanxiang. Research of multi-information fusion technology in transformer fault diagnosis[D]. Beijing: North China Electric Power University, 2013.
[17] 翟少磊,曹敏,沈鑫,等. 变电站在线监测多维信息聚合技术[J]. 高电压技术,2015,41（12）：3973-3979.
ZHAI Shaolei, CAO Min, SHEN Xin, et al . Multidimensional information of aggregation technology for on-line monitoring of substation[J]. High Voltage Engineering, 2015, 41(12): 3973-3979.
[18] 谢龙君,李黎,程勇,等. 融合集对分析和关联规则的变压器故障诊断方法[J]. 中国电机工程学报,2015,35（2）：277-286.
XIE Longjun, LI Li, CHENG Yong, et al . A fault diagnosis method of power transformers by integrated set pair analysis and association rules [J]. Proceedings of the CSEE, 2015, 35(2): 277-286.
[19] LI L, CHENG Y, XIE L J, et al . An integrated method of set pair analysis and association rule for fault diagnosis of power transformers[J]. IEEE Transactions on Dielectrics & Electrical Insulation, 2015, 22(4): 2368-2378.
[20] GONG Y S, ZHANG B. The fault diagnosis model of transformer which based on the technology of information fusion[C]//IEEE Ninth International Conference on//Hybrid Intelligent Systems,IEEE, 2009: 184-187.
[21] 秦伟,周源. 变压器油纸电容式套管故障诊断实例分析[C]//2010年江苏省电机工程学会高电压技术学术年会,2010.

基于多维度信息融合的实用型变压器故障诊断专家系统

Practical Expert System for Transformer Fault Diagnosis Based on Multi-Dimensional Information Fusion Technology

RichHTML

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	刘贺千, 张健, 张海月, 杨洪达, 陈世玉, 申昱博, 王磊. 基于高频阻抗谱的干式空心电抗器匝间短路故障诊断方法[J]. 中国电力, 2024, 57(10): 218-224.
[2]	黎燕, 程馨, 黄祖梁, 杨琢. 高可靠性三相多功能并网变换器研究[J]. 中国电力, 2023, 56(5): 172-181.
[3]	辛全金, 李晓华, 杨义, 李俊聪, 夏能弘. 基于冗余卷积编解码器的变压器噪声抑制[J]. 中国电力, 2023, 56(4): 112-118.
[4]	李刚, 孟坤, 贺帅, 刘云鹏, 杨宁. 考虑特征耦合的Bi-LSTM变压器故障诊断方法[J]. 中国电力, 2023, 56(3): 100-108,117.
[5]	易亚文, 江传宾, 龚世玉, 秦小元, 赵静朴, 李振兴. 基于因子分析与统计学技术的保护测量回路误差评估[J]. 中国电力, 2023, 56(12): 183-190.
[6]	韩伟, 段文岩, 杜兴伟, 姚峰, 马伟东, 刘磊. 基于数字孪生的在运安控系统故障诊断方法[J]. 中国电力, 2023, 56(11): 121-127.
[7]	马海飞, 滕伟, 彭迪康, 柳亦兵, 靳涛. 基于DRS与改进Autogram的风电齿轮箱复合故障特征提取[J]. 中国电力, 2023, 56(10): 71-79.
[8]	王爽, 罗倩, 唐波, 姜岚, 李锦. 考虑样本类内不平衡的CHPOA-DBN变压器故障诊断方法[J]. 中国电力, 2023, 56(10): 133-144.
[9]	张宏杰, 陈贵凤, 闫宏伟, 杨晓龙, 侯天仁, 张伟. 基于SMOTE与Bayes优化的LSTM网络变压器故障诊断[J]. 中国电力, 2023, 56(10): 164-170.
[10]	陈铁, 冷昊伟, 李咸善, 陈一夫. 基于油中气体分析与类重叠特征的变压器分层故障诊断模型[J]. 中国电力, 2022, 55(7): 22-32,41.
[11]	李天辉, 庞先海, 范辉, 甄利, 顾朝敏, 董驰. 基于IEMD和GA-WNN的断路器分合闸线圈故障诊断方法[J]. 中国电力, 2022, 55(5): 111-121.
[12]	郝玲玲, 朱永利, 王永正. 基于DCAE-KSSELM的变压器故障诊断方法[J]. 中国电力, 2022, 55(2): 125-130.
[13]	余斌, 宋兴荣, 周挺, 罗林波, 李辉, 车亮. 基于梅尔倒谱系数特征集的储能变流器开路故障诊断方法[J]. 中国电力, 2022, 55(12): 34-42.
[14]	杜旭浩, 李秉宇, 苗俊杰, 郭小凡. 分布式储能电池运行工况及性能检测分析[J]. 中国电力, 2021, 54(9): 119-124.
[15]	李福志, 郑卫宾, 张文海, 张智勇, 林德锋, 张旭波, 张荣建. 基于回路直流电阻测量的输电线路单相接地故障离线故障定位[J]. 中国电力, 2021, 54(2): 140-146.

模态框（Modal）标题

基于多维度信息融合的实用型变压器故障诊断专家系统

Practical Expert System for Transformer Fault Diagnosis Based on Multi-Dimensional Information Fusion Technology

RichHTML

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics