Journal of East China Normal University(Natural Science) ›› 2021, Vol. 2021 ›› Issue (6): 135-146.doi: 10.3969/j.issn.1000-5641.2021.06.014

• Computer Science • Previous Articles    

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

Guofang ZHANG1(), Lili WEN1, Meng WU1, Tongyu LIU2, Kuanyun ZHENG3, Fuxing HUANG3, Peisen YUAN2,*()   

  1. 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:2020-09-11 Online:2021-11-25 Published:2021-11-26
  • Contact: Peisen YUAN;


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

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