AUTHOR=Xie Tenglong , Li Bo , Zhen Yingkai , Wei Hao , Xu Kunhuan , Huang Jingyin 
  
TITLE=Anomaly detection method for power dispatch streaming data based on adaptive isolation forest and self-supervised learning
  
JOURNAL=Frontiers in Physics
  
VOLUME=Volume 13 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2025.1704495
  
DOI=10.3389/fphy.2025.1704495
  
ISSN=2296-424X
  
ABSTRACT=IntroductionTo address the issues of concept drift and scarcity of anomaly samples in real-time anomaly detection under the massive streaming data environment of power dispatching and control systems, this study focuses on developing an effective detection method.MethodsWe propose a streaming data anomaly detection method integrating adaptive isolation forests and self-supervised learning. First, a business-based model is constructed by analyzing inherent relationships between system services, processes, and resource usage. An improved isolation forest algorithm with a sub-forest progressive update mechanism is designed—selectively eliminating sub-detectors with large anomaly rate deviations and dynamically adding new ones to overcome performance degradation from traditional random updates. Additionally, a GPT-based self-supervised learning framework is introduced, incorporating state memory units to encode historical data patterns and a distance metric-based sampling strategy to reduce redundancy.ResultsExperiments on a real power dispatching process resource dataset show the proposed method significantly outperforms the traditional streaming data isolation forest algorithm in key indicators such as AUC value, with the highest improvement reaching 39.12%. Ablation experiments verify the effectiveness of each module.DiscussionThe proposed method enhances the detection algorithm's adaptability to concept drift, overall stability, and ability to perceive hidden anomalies, providing reliable technical support for the safe and stable operation of the power dispatching system.