Journal of East China Normal University(Natural Science) >

2021 , Vol. 2021 >Issue 6: 135 - 146

DOI: https://doi.org/10.3969/j.issn.1000-5641.2021.06.014

Computer Science

Anomaly detection of transformer loss data based on a robust random cut forest

  • Guofang ZHANG ,
  • Lili WEN ,
  • Meng WU ,
  • Tongyu LIU ,
  • Kuanyun ZHENG ,
  • Fuxing HUANG ,
  • Peisen YUAN
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  • 1. State Grid Sichuan Electric Power Company, Chengdu　610094, China
    2. College of Artificial Intelligence, Nanjing Agricultural University, Nanjing　210095, China
    3. Nanjing Automatic Research Insititute Group Corporation (State Grid Electric Power Research Institute), Nanjing　211106, China

Received date: 2020-09-11

  Online published: 2021-11-26

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Abstract

With the rapid development of smart grids, the construction of new digital infrastructure has become one of the core businesses of power companies. Power companies’ governance and intelligent analytical capabilities enable opportunities for business model innovation, such as platform operation and value-added data realization. In the context of power digitization and intelligent governance, we use the robust random cut forest in this paper for transformer loss data anomaly intelligence detection. The algorithm divides sample points by random cutting to construct a random cut forest structure model by inserting and removing sample points in the structure; the anomaly score of a sample point is then given by the influence of complexity. This method is suitable for anomaly detection on real-time loss data and offers a high degree of credibility, effectiveness, and efficiency. An experiment of anomaly detection on real transformer loss data shows that the method is efficient and flexible. The accuracy, recall, and efficiency of the proposed method, moreover, is substantially better than alternatives.

Key words： robust random cut forest; anomaly detection; transformer loss; power data platform

Cite this article

Guofang ZHANG , Lili WEN , Meng WU , Tongyu LIU , Kuanyun ZHENG , Fuxing HUANG , Peisen YUAN . Anomaly detection of transformer loss data based on a robust random cut forest[J]. Journal of East China Normal University(Natural Science), 2021 , 2021(6) : 135 -146 . DOI: 10.3969/j.issn.1000-5641.2021.06.014

References

1 王忠杰, 文乐, 杨新民. 大数据在智能化电厂中的应用研究与展望. 中国电力, 2019, 52 (3): 133- 139.
2 李炳森, 胡全贵, 陈小峰, 等. 电网企业数据中台的研究与设计. 电力信息化, 2019, 17 (7): 29- 34.
3 林鸿, 方学民, 袁葆, 等. 电力物联网多渠道客户服务中台战略研究与设计. 供用电, 2019, 36 (6): 39- 45.
4 SUNDARARAJAN A, HERNANDEZ A S, SARWAT A I. Adapting big data standards, maturity models to smart grid distributed generation: Critical review. IET Smart Grid, 2020, 3 (4): 508- 519.
5 PASSERINI F, TONELLO A M. Smart grid monitoring using power line modems: Effect of anomalies on signal propagation. IEEE Access, 2019, (7): 27302- 27312.
6 刘树仁, 宋亚奇, 朱永利, 等. 基于Hadoop的智能电网状态监测数据存储研究. 计算机科学, 2013, 40 (1): 81- 84.
7 HUO Y, PRASAD G, ATANACKOVIC L, et al. Cable diagnostics with power line modems for smart grid monitoring. IEEE Access, 2019, (7): 60206- 60220.
8 WITTEN I H, FRANK E, HALL M A, et al. Data Mining: Practical Machine Learning Tools and Techniques [M]. 4th ed. San Francisco: Morgan Kaufmann, 2016.
9 COSTA D, PORTELA F, SANTOS M F. An overview of data mining representation techniques [C]// Proceedings of the 2019 7th International Conference on Future Internet of Things and Cloud Workshops. IEEE, 2019: 90-95.
10 AKOGLU L, TONG H, KOUTRA D. Graph based anomaly detection and description: A survey. Data Mining & Knowledge Discovery, 2015, 29 (3): 626- 688.
11 CHANDOLA V, BANERJEE A, KUMAR V. Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge & Data Engineering, 2012, 24 (5): 823- 839.
12 TRAN T N, DRAB K, DASZYKOWSKI M. Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometrics & Intelligent Laboratory Systems, 2013, 120, 92- 96.
13 王文红, 李惊涛, 陈俊彦, 等. 基于聚类算法对异常事件分析评价电能表整体状态的方法: CN201310624924.4 [P]. 2014-03-12.
14 LIU F T, TING K M, ZHOU Z. Isolation forest [C]// 2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008: 413-422.
15 余翔, 陈国洪, 李霆, 等. 基于孤立森林算法的用电数据异常检测研究. 信息技术, 2018, 42 (12): 88- 92.
16 GUHA S, MISHRA N, ROY G, et al. Robust random cut forest based anomaly detection on streams [C]// International Conference on Machine Learning. PMLR, 2016: 2712-2721.
17 INOUE J, YAMAGATA Y, CHEN Y, et al. Anomaly detection for a water treatment system using unsupervised machine learning [C]// Proceedings of the 2017 IEEE International Conference on Data Mining Workshops. IEEE, 2017: 1058-1065.
18 BARTOS M, MULLAPUDI A, TROUTMAN S. RRCF: Implementation of the robust random cut forest algorithm for anomaly detection on streams. Journal of Open Source Software, 2019, 4 (35): 1336.
19 WANG Y, WANG Z, XIE Z, et al. Practical and white-box anomaly detection through unsupervised and active learning [C]// 2020 29th International Conference on Computer Communications and Networks. IEEE, 2020. DOI: 10.1109/ICCCN49398.2020.9209704.
20 BOX G E P, JENKINS G M, REINSEL G C, et al. Time series analysis: Forecasting and control. Journal of the Operational Research Society, 2015, 22 (2): 199- 201.
21 HABEEB R A A, NASARUDDIN F, GANI A, et al. Real-time big data processing for anomaly detection: A survey. International Journal of Information Management, 2019, 45, 289- 307.
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