基于VAE的核电运行状态监测方法

由于核反应堆发电的特殊性,核电厂对于生产安全的敏感度远胜于普通电厂。作为日常运维的重要环节,核电机组运行状态监测,对于核电厂的安全稳定运行具有重要意义。当前核电机组状态监测主要采用预设固定阈值报警结合人工监盘的方式,这种方式无法发现低于报警阈值的异常状态,同时存在一定程度的漏报风险。核电运行数据作为高维海量时序数据,具有正常样本和异常样本分布不均衡以及数据缺乏标签的问题,这限制了有监督深度学习方法的使用。本文提出了一种基于变分自编码器（Variational Auto-Encoders,VAE）构建的无监督深度学习模型对真实运行数据进行异常检测,通过正常运行数据学习正常模式下数据在隐空间的分布,并基于异常数据无法被良好重构的原理,通过重构误差的大小来判别当前状态是否异常。实验以核电机组化学和容积控制系统（Chemical and Volume Control System,RCV）中的上充泵为例,使用真实运行数据结合插入异常的方式对模型进行了验证,并与经典机器学习方法进行了对比。实验结果表明基于变分自编码器的模型能够有效检测到核电真实数据中的异常数据片段及离群点,检测精确率和召回率均高于90%,检测性能相对孤立森林和支持向量机等经典机器学习算法具有优势,具备一定的实用价值和研究意义。

Research on VAE-based Operation State Monitoring of Nuclear Power Plants

Due to the unique power generation method used in nuclear reactors, nuclear power plants are more safety-sensitive compared to conventional power plants. Therefore, daily monitoring of the operating state of nuclear power units is critical for ensuring operational safety. Currently, status monitoring of nuclear power plants is conducted through automated alarms with preset fixed thresholds. and manual supervision. However, this method cannot detect anomalies below the alarm thresholds, which may lead to risks of underreporting. Nuclear power operational data, characterized by high-dimensional time series, faces challenges of imbalanced distributions between normal and abnormal samples as well as the lack of labeled data. These factors limit the application of supervised deep learning methods. In this paper, we propose an unsupervised deep learning model based on Variational Autoencoders (VAE) for anomaly detection in real operational data. This model learns the distribution of data in the latent space under normal operating conditions and relies on the principle that abnormal data cannot be reconstructed effectively. Anomalies are detected by evaluating the magnitude of reconstruction error. The experiment focused on the upper charging pump in the chemical and volume control system (RCV) of a nuclear power plant. It involved the validation of the model using real operational data with deliberately inserted anomalies and compared it to classical machine learning methods. The results of the experiment show that the model based on Variational Autoencoders effectively detects abnormal data segments and outliers in real nuclear power plant data. It achieves precision and recall rates both exceeding 90%. In terms of detection performance, it outperforms classical machine learning algorithms like Isolation Forest and support vector machine (SVM). This demonstrates its practical value and research significance.