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基于大数据分析挖掘技术的电力设备局部放电诊断方法
引用本文:魏丽峰,韩俊玉,梁灏.基于大数据分析挖掘技术的电力设备局部放电诊断方法[J].科学技术与工程,2018,18(19).
作者姓名:魏丽峰  韩俊玉  梁灏
作者单位:国网山西省电力公司检修分公司
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。

关 键 词:大数据分析  挖掘  电力设备  局部放电  诊断
收稿时间:2017/12/18 0:00:00
修稿时间:2018/3/7 0:00:00

Partial Discharge Diagnosis Method for Power Equipment Based on Big Data Analysis and Mining Technology
WEI Lifeng,HAN Junyu and LIANG Hao.Partial Discharge Diagnosis Method for Power Equipment Based on Big Data Analysis and Mining Technology[J].Science Technology and Engineering,2018,18(19).
Authors:WEI Lifeng  HAN Junyu and LIANG Hao
Institution:Maintenance Division of State Grid Shanxi Electric Power Company,Maintenance Division of State Grid Shanxi Electric Power Company,Maintenance Division of State Grid Shanxi Electric Power Company
Abstract: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.
Keywords:big data analysis  mining  power equipment  partial discharge  diagnosis
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