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基于FP-growth算法的用電異常數(shù)據(jù)挖掘方法
2020年電子技術(shù)應(yīng)用第10期
段曉萌1,王 爽1,,趙 婷1,,丁徐楠2
1.中國電力科學研究院有限公司,,北京100192;2.國網(wǎng)浙江省電力有限公司,,浙江 杭州310007
摘要: 隨著科學技術(shù)的不斷進步,,不法分子竊電手段日趨專業(yè)化多樣化,而傳統(tǒng)的防竊電技術(shù)實時性及可行性較低。研究對運行中智能電能表用電信息的數(shù)據(jù)采集及特征提取,,分析異常用電數(shù)據(jù),,應(yīng)用機器學習的方法對特征值進行學習,并推導出用電異常的判斷閾值,,采用關(guān)聯(lián)規(guī)則數(shù)據(jù)挖掘方法對獨立檢測的結(jié)果進行融合,,從而實現(xiàn)竊電數(shù)據(jù)的挖掘。最后驗證了模型建立的準確性,,并推導出用電異常案例的甄別方法,。
中圖分類號: TN915;TM933
文獻標識碼: A
DOI:10.16157/j.issn.0258-7998.200073
中文引用格式: 段曉萌,,王爽,,趙婷,等. 基于FP-growth算法的用電異常數(shù)據(jù)挖掘方法[J].電子技術(shù)應(yīng)用,,2020,,46(10):47-50.
英文引用格式: Duan Xiaomeng,Wang Shuang,,Zhao Ting,,et al. Data mining method on abnormal electricity usage based on FP-growth algorithm[J]. Application of Electronic Technique,2020,,46(10):47-50.
Data mining method on abnormal electricity usage based on FP-growth algorithm
Duan Xiaomeng1,,Wang Shuang1,Zhao Ting1,,Ding Xunan2
1.China Electric Power Research Institute,,Beijing 100192,China,; 2.State Grid Zhejiang Electric Power Co.,,Ltd.,Hangzhou 310007,,China
Abstract: Because of the technology development, the means for stealing electricity becomes more specialized and diversified. The traditional anti-theft technology is less real-time and less feasible. This paper studied the intelligent diagnosis and characteristics extract method of electricity energy meter during online operation, analyzed the abnormal electricity consumption data, used machine learning abnormality judgment thresholds based on features, and used association rule data mining methods to fuse independent detection results, realizing the mining of power theft data. At last, this paper verified the accuracy of the model establishment, and deduced the screening method of power consumption abnormal cases.
Key words : energy meter,;abnormal electricity usage;FP-growth algorithm,;data mining

0 引言

    電能表電能計量的準確性是電網(wǎng)公司與電力用戶之間貿(mào)易結(jié)算及電網(wǎng)公司利潤實現(xiàn)的最終環(huán)節(jié),,不法行為會嚴重傷害貿(mào)易關(guān)系的公平、公正,、公開性,,因此查處用電異常行為是電網(wǎng)公司一直以來的工作重點。隨著電網(wǎng)公司對反竊電工作重視程度的增加,,不法分子的手段也逐步變得隱蔽化與智能化[1],。近年來,,隨著用電信息采集系統(tǒng)的不斷完善,已經(jīng)能夠按照業(yè)務(wù)需求廣泛采集到電能表的大量數(shù)據(jù),,從大量無序數(shù)據(jù)中應(yīng)用單一準則判斷用電異常,,容易產(chǎn)生誤判情況,如由于環(huán)境或振動而引發(fā)的開表蓋事件[2],。如何從大量的用電異常數(shù)據(jù)中提高辨別竊電數(shù)據(jù)的概率,,從多組數(shù)據(jù)關(guān)聯(lián)來推斷是否竊電,是本文研究的重點,。因此提出一種通過數(shù)據(jù)關(guān)聯(lián)規(guī)則判斷在運電能表用電異常行為的數(shù)據(jù)挖掘方法,。




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作者信息:

段曉萌1,王  爽1,,趙  婷1,,丁徐楠2

(1.中國電力科學研究院有限公司,北京100192,;2.國網(wǎng)浙江省電力有限公司,浙江 杭州310007)

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