Introduction  The safe operation of nuclear power equipment is crucial for nuclear power plants (NPPs), and the losses caused by accidents are immeasurable. Therefore, effective anomaly detection for nuclear power equipment is necessary. Considering the limitations of fixed thresholds and manual detection methods, which are difficult to adapt to the dynamic changes in time series data, this paper proposes an anomaly detection method based on POT for multivariate statistical processes.

  Method  This paper adopted PCA to construct an anomaly detection model, where the SPE statistic of the model served as the initial threshold for the POT algorithm. Subsequently, the portion exceeding the initial threshold was fitted with a generalized Pareto distribution to determine the final dynamic threshold. An anomaly warning was issued when the anomaly score exceeded the final threshold. By combining multivariate statistical process control (MSPC) with extreme value theory (EVT), this method used MSPC to discover anomalies in the operating data of NPPs quickly and improved the sensitivity and reliability of anomaly detection by modeling and analyzing extreme events, so that it can quickly detect anomalies in high-dimensional operating data of NPPs.

  Result  In the simulation experiment results, the proposed method has a higher accuracy and recall rate than conventional multivariate statistical and POT methods. In experiments with actual operating data from different equipment in NPPs, the method's effectiveness in anomaly detection has been demonstrated.

  Conclusion  By combining MPSC with EVT, the anomaly detection method proposed in this paper can not only detect anomalies caused by changes in data relationships but also avoid false detection in traditional MSPC by determining the final threshold using the POT method. This method can handle high-dimensional time series operating data of NPPs, improve the efficiency of anomaly detection, ensure the safe and efficient operation of NPPs, and improve their economic benefits.