[1]马 刚,吴克河,李 艺.一种电气设备状态诊断方法[J].南京师范大学学报(工程技术版),2014,14(03):007.
 Ma Gang,Wu Kehe,Li Yi.An Intelligent Fault Diagnosis Model of Power Equipment[J].Journal of Nanjing Normal University(Engineering and Technology),2014,14(03):007.
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一种电气设备状态诊断方法
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
14卷
期数:
2014年03期
页码:
007
栏目:
出版日期:
2014-09-30

文章信息/Info

Title:
An Intelligent Fault Diagnosis Model of Power Equipment
作者:
马 刚1吴克河2李 艺2
(1.南京师范大学电气与自动化工程学院,江苏 南京 210042)(2.华北电力大学控制与计算机工程学院,北京 102206)
Author(s):
Ma Gang1Wu Kehe2Li Yi2
(1.School of Electrical and Automation Engineering,Nanjing Normal University,Nanjing 210042,China)(2.School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China)
关键词:
电气设备状态诊断范例推理SVM回归分析核函数状态指纹
Keywords:
diagnosis of power equipmentcase-based reasoningSVM regression analysiskernel functioncondition fingerprint
分类号:
TM407
文献标志码:
A
摘要:
随着电网技术的不断发展和电网规模不断扩大,电气设备数量激增、智能化程度越来越高; 同时,终端用户对用电可靠性越来越重视,借助智能技术基于设备运行数据对设备进行故障诊断势在必行.本文以基于范例推理的理论(CRB)与支持向量机技术(SVM)为主要工具,提出了一种基于范例推理的电气设备状态智能诊断模型,试图通过电气设备已有数据的挖掘,获取电气设备故障的潜在发生规律,进而作为依据及时发现并排除电气设备的潜伏性故障.首先研究CRB和SVM在电气设备状态诊断中的应用; 然后建立电气设备状态智能诊断模型,以电气设备的海量运行数据、历史数据、测试数据以及环境因素等为基础,建立电气设备的状态范例库,应用SVM回归对设备状态范例库进行深度的挖掘与分析,建立设备状态指纹,并以此为据进行电气设备运行状态的诊断分析; 最后以油浸式变压器状态诊断为例,对实际数据进行分析诊断,并与三比值法的诊断结果进行比较.诊断结果表明,智能诊断模型诊断范围更广,诊断结果更准确.
Abstract:
With the rapid development of power system technology and increasing expansion of the grid size,the number of electrical equipment surges and the degree of intelligentialization becomes higher and higher; meanwhile,the consumers pay more and more attention to the reliability of power utilization,and thus it is imperative that equipment running data-based fault diagnosis is made on equipment with the help of intelligent technology.An intelligent fault diagnosis model of transmission and transformation equipment based on CRB and SVM is proposed in this paper.The model tries to find the potential rules of equipment fault by digging the existing data.The intelligent model sets up condition case base of equipment based on online recording data,history data,basic test data and environmental data.SVM regression analysis is used to mine the case base so that the equipment condition fingerprint is established.The running data of equipment can be diagnosed by the condition fingerprint to determine whether there is a fault.At last,we diagnose a set of practical data with the intelligent model and three-ratio method.The result shows that intelligent model is more effective and accurate.

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相似文献/References:

[1]邢乃宁,高红梅,孙志挥.一种综合范例推理和规则推理的发现技术[J].南京师范大学学报(工程技术版),2001,01(01):036.
 Xing Naining,Gao Hongmei,Sun Zhihui.Integrated Discovering Technology of Case-Based Reasoning and Rule Induction[J].Journal of Nanjing Normal University(Engineering and Technology),2001,01(03):036.

备注/Memo

备注/Memo:
收稿日期:2014-05-21.
基金项目:江苏省自然科学基金(BK20141452)、江苏省高校自然科学研究项目(14KJB470006)、南京师范大学高层次人才科研启动研究项目(2014111XGQ0078).
通讯联系人:马刚,博士,讲师,研究方向:电力系统自动化,人工智能及应用.E-mail:nnumg@njnu.edu.cn
更新日期/Last Update: 2014-09-30