Research on Historical Anomaly Data Detection Technology for Nuclear Power Plant Based on Deep Auto-Encoder

In order to solve the problem of new anomaly identification difficulties in the detection of nuclear power historical anomaly data, according to the idea of reconstruction error, an anomaly detection model based on deep auto-encoder is proposed. The model takes the normal historical data under steady-state operating condition as the learning object, trains itself by minimizing the reconstruction error of the normal data, and judges whether the unknown data is abnormal according to the size of the reconstruction error. The research results show that the deep autoencoder has better ability to reconstruct normal data but insufficient ability to reconstruct abnormal data. Thus, by comparing the reconstruction error size, the deep autoencoder can effectively detect the historical abnormal data of nuclear power plant, and its performance is better than that of one-class support vector machine, which can provide relevant basis for the state evaluation of nuclear power plants.