基于大数据分析挖掘技术的电力设备局部放电诊断方法
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摘要
当前电力设备状态参量规模逐渐增加,电力设备状态数据来自多个不同系统,较为复杂。传统诊断方法不能有效处理大规模数据,导致电力设备局部放电诊断结果不可靠。为此,提出一种新的基于大数据分析挖掘技术的电力设备局部放电诊断方法。给出谱图生成过程,对电力设备局部放电特征进行提取。对电力设备状态参量进行大数据分析挖掘,完成对电力设备各种状态参量的组合、特征合并处理。通过皮尔逊相关性理论计算相关系数。依据相关系数,利用优化迭代将电力设备样本分成若干类,获取对应聚类中心。将局部放电样本聚集在一起,依据局部放电特征实现诊断。实验结果表明,所提方法诊断可靠性与实用性强。
At present,the state parameters of power equipment gradually increase in size,and the power equipment status data comes from many different systems,which is more complicated. Traditional diagnostic methods can not effectively deal with large-scale data,resulting in partial discharge diagnosis of electrical equipment is not reliable. Therefore,a new partial discharge diagnosis method for power equipment based on big data analysis and mining technology is proposed. The spectrum generation process is given,and the partial discharge characteristics of power equipment are extracted. The state parameters of power equipment are analyzed by big data mining,the combination of various state parameters of power equipment and the feature merging are completed,and the correlation coefficient is calculated by Pearson correlation theory. According to the correlation coefficient,the power equipment samples are divided into several classes by using the optimized iteration,the corresponding cluster centers are obtained,the partial discharge samples are gathered together,and the diagnosis is realized according to the partial discharge characteristics. The experimental results show that the proposed method has high reliability and practicability.
At present,the state parameters of power equipment gradually increase in size,and the power equipment status data comes from many different systems,which is more complicated. Traditional diagnostic methods can not effectively deal with large-scale data,resulting in partial discharge diagnosis of electrical equipment is not reliable. Therefore,a new partial discharge diagnosis method for power equipment based on big data analysis and mining technology is proposed. The spectrum generation process is given,and the partial discharge characteristics of power equipment are extracted. The state parameters of power equipment are analyzed by big data mining,the combination of various state parameters of power equipment and the feature merging are completed,and the correlation coefficient is calculated by Pearson correlation theory. According to the correlation coefficient,the power equipment samples are divided into several classes by using the optimized iteration,the corresponding cluster centers are obtained,the partial discharge samples are gathered together,and the diagnosis is realized according to the partial discharge characteristics. The experimental results show that the proposed method has high reliability and practicability.
引文
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15 Pattanapairoj S,Silsirivanit A,Muisuk K,et al.Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach.Clinical Biochemistry,2015;48(10-11):668-673
16王德文,刘晓建.基于MapReduce的电力设备并行故障诊断方法.电力自动化设备,2014;34(10):116-120Wang Dewen,Liu Xiaojian.Parallel fault diagnosis based on MapReduce for electric power equipments.Electric Power Automation Equipment,2014;34(10):116-120
17张少敏,毛冬,王保义.大数据处理技术在风电机组齿轮箱故障诊断与预警中的应用.电力系统自动化,2016;40(14):129-134Zhang Shaomin,Mao Dong,Wang Baoyi.Application of big data processing technology in fault diagnosis and early warning of wind turbine gearbox.Automation of Electric Power Systems,2016;40(14):129-134
18张金春,张家宾,李超亚,等.基于可拓数据挖掘技术的故障诊断和预防方法.兵工自动化,2015;34(5):1-4Zhang Jinchun,Zhang Jiabin,Li Chaoya,et al.Methods of fault diagnosis and prevention of equipment based on extenics and data mining technology.Ordnance Industry Automation,2015;34(5):1-4
2孙毅刚,曲睿,陈维兴,等.面向数据挖掘的静态电源综合故障诊断研究.计算机测量与控制,2015;23(10):3274-3276Sun Yigang,Qu Rui,Chen Weixing,et al.Research of data mining oriented integrated error diagnosis of solid state power.Computer Measurement&Control,2015;23(10):3274-3276
3杨世海,戴太文,卢树峰,等.基于数据挖掘的计量装置在线监测与故障诊断系统设计.电子设计工程,2016;24(23):108-111Yang Shihai,Dai Taiwen,Lu Shufeng,et al.Design of online monitoring and fault diagnosis system for metering devices based on data mining.Electronic Design Engineering,2016;24(23):108-111
4 Gebremeskel G B,Yi C,Wang C,et al.Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques.Industrial Management&Data Systems,2015;115(6):1151-1178
5孙勤红.基于梯度采样局部收敛的生物信息大数据挖掘.科技通报,2015;31(10):214-216Sun Qinhong.Large data mining in biological information based on gradient sampling local convergence.Bulletin of Science and Technology,2015;31(10):214-216
6陈敏维,陈彬,叶兆平,等.采用物联网技术的局部放电检测系统.中国电力,2017;50(8):150-157Chen Minwei,Chen Bin,Ye Zhaoping,et al.System of partial discharge detection using Io T technology.Electric Power,2017;50(8):150-157
7郑茂然,余江,陈宏山,等.基于大数据的输电线路故障预警模型设计.南方电网技术,2017;11(4):30-37Zheng Maoran,Yu Jiang,Chen Hongshan,et al.Design of fault warning model of transmission line based on big data.Southern Power System Technology,2017;11(4):30-37
8李涛,林陈,王丽娜.一种改进的相关项对挖掘算法研究.计算机仿真,2016;33(8):223-228Li Tao,Lin Chen,Wang Lina.An improved algorithm research on mining correlation pairs.Computer Simulation,2016;33(8):223-228
9 Chang H C,Lin S C,Kuo C C,et al.Fuzzy theory-based partial discharge technique for operating state diagnosis of high-voltage motor.International Journal of Fuzzy Systems,2016;18(6):1-12
10梅毅,熊婷,罗少彬.复杂属性环境下No SQL分布式大数据挖掘方法研究.科学技术与工程,2017;17(9):239-243Mei Yi,Xiong Ting,Luo Shaobin.Research on No SQL distributed Big data mining method in complex attribute environment.Science Technology and Engineering,2017;17(9):239-243
11王刘旺,朱永利,贾亚飞.一种多源海量局部放电信号脉冲的并行提取方法.系统仿真学报,2017;29(1):57-66Wang Liuwang,Zhu yongli,Jia Yafei.Parallel method for extracting pulses from multi-source massive partial discharge signals.Journal of System Simulation,2017;29(1):57-66
12 Kausar N,Palaniappan S,Samir B B,et al.Systematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patients.Electrochimica Acta,2016;53(18):5704-5710
13曹旭,曹瑞彤.基于大数据分析的网络异常检测方法.电信科学,2014;30(6):152-156Cao Xu,Cao Ruitong.Network anomaly prediction method based on big data.Telecommunications Science,2014;30(6):152-156
14马丽强,王天正,杨冬冬,等.基于弱关联挖掘技术的电网故障自动诊断研究.现代电子技术,2016;39(10):152-155Ma Liqiang,Wang Tianzheng,Yang Dongdong,et al.Research on power grid fault automatic diagnosis based on weak association mining technology.Modern Electronics Technique,2016;39(10):152-155
15 Pattanapairoj S,Silsirivanit A,Muisuk K,et al.Improve discrimination power of serum markers for diagnosis of cholangiocarcinoma using data mining-based approach.Clinical Biochemistry,2015;48(10-11):668-673
16王德文,刘晓建.基于MapReduce的电力设备并行故障诊断方法.电力自动化设备,2014;34(10):116-120Wang Dewen,Liu Xiaojian.Parallel fault diagnosis based on MapReduce for electric power equipments.Electric Power Automation Equipment,2014;34(10):116-120
17张少敏,毛冬,王保义.大数据处理技术在风电机组齿轮箱故障诊断与预警中的应用.电力系统自动化,2016;40(14):129-134Zhang Shaomin,Mao Dong,Wang Baoyi.Application of big data processing technology in fault diagnosis and early warning of wind turbine gearbox.Automation of Electric Power Systems,2016;40(14):129-134
18张金春,张家宾,李超亚,等.基于可拓数据挖掘技术的故障诊断和预防方法.兵工自动化,2015;34(5):1-4Zhang Jinchun,Zhang Jiabin,Li Chaoya,et al.Methods of fault diagnosis and prevention of equipment based on extenics and data mining technology.Ordnance Industry Automation,2015;34(5):1-4