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

doi:10.3969/j.issn.1000-5641.2021.06.014

Abstract

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

CLC Number:

TP391

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.

Figures/Tables 8

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初始化样本点个数	准确率	精确率	召回率
60	0.93	0.72	0.76
70	0.94	0.80	1.00
80	0.96	0.92	1.00
90	0.97	0.90	1.00
100	0.97	0.93	1.00
110	0.96	0.87	1.00
120	0.96	0.83	1.00

方法	检测到异常点个数	准确率	精确率	召回率	F₁值
本文所述方法	19	0.9677	0.9315	1.0000	0.9637
孤立森林算法	24	0.9523	0.7619	1.0000	0.8649
基于DBSCAN聚类的方法	12	0.9219	0.9167	0.7500	0.8250

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