940 / 2019-04-30 19:00:49
Anomaly Detection and Processing in Artificial Intelligence for IT Operations of Power System
AIOps, anomaly detection, machine learning, statistical analysis
全文录用
Yiyu Xia / NARI Group Corporation (State Grid Electric Power Research Institute)
Jixiang Lu / NARI Group Corporation (State Grid Electric Power Research Institute)
Chunlei Xu / State Grid Jiangsu Electric Power co., Ltd
Yun Li / NARI Group Corporation (State Grid Electric Power Research Institute)
Bin Zhang / NARI Group Corporation (State Grid Electric Power Research Institute)
Hao Li / NARI Group Corporation (State Grid Electric Power Research Institute)
Feng Xie / NARI Group Corporation (State Grid Electric Power Research Institute)
Shaobo Liu / NARI Group Corporation (State Grid Electric Power Research Institute)
In recent years anomaly detection has been wildly applied in many fields, from zero day attack detection to insider threat detection, from situational awareness to intrusion detection. For power system, secure and stable operation is indispensable and it takes electric utility staff huge amount of time. Naturally, artificial intelligence for IT operations (AIOps) especially anomaly detection can also be used to find out unusual behavior discord with expected pattern in this field. In this paper, we propose an intelligent system that first conducts a joint time series detection to identify outliers or anomalies on the basis of statistical judgment and machine learning, and then automatically discovers those anomalous functions in the method of statistical analysis. The result indicates that the implementation of our system is able to largely reduce labor costs, improve automation and efficiency of power system operations and maintenance.
重要日期
  • 会议日期

    10月21日

    2019

    10月24日

    2019

  • 10月13日 2019

    摘要录用通知日期

  • 10月13日 2019

    初稿截稿日期

  • 10月14日 2019

    初稿录用通知日期

  • 10月24日 2019

    注册截止日期

  • 10月29日 2019

    终稿截稿日期

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