蒸汽发生器液位是评价核电机组运行状态的重要参数指标之一,由于传统预设固定液位报警阈值的监测方法无法在触发报警信号前及早发现异常,为蒸汽发生器液位建立异常检测模型很有必要.基于蒸汽发生器复杂非线性系统的特点,通过带外源输入的非线性自回归(nonlinear auto-regressive with exogenous inputs,NARX)方法研究了蒸汽发生器在正常工作模式下液位及相关参数间的耦合关系模型.模型以历史液位值和相关参数作为输入回归得到下一时刻的液位预测值,并通过预测值与实际观测值残差的大小,来判断蒸汽发生器多传感器系统当前工作状态是否异常.与触发预设液位阈值后再报警的传统状态监测方法相比,结果表明该方法能够检测到液位与相关参数间的耦合关系偏移,并在微小变化发生时就检测到异常,从而实现蒸汽发生器液位的状态监测和预警.同时经真实核电厂数据验证,可见该模型能够对液位实现准确的回归预测,并在依照真实故障类型构建的异常数据集验证实验中,取得了较好的异常检测效果.
Anomaly Detection of Steam Generator Water Level Based on NARX
The liquid level of a steam generator is one of the key parameters for evaluating the operational status of a nuclear power plant.Traditional monitoring methods with preset fixed liquid level alarm thresholds fail to detect abnormalities early enough before triggering alarm signals.Therefore,it is necessary to establish an anomaly detection model for the steam generator liquid level.Considering the complex nonlinear characteristics of steam generator systems,NARX(nonlinear auto-regressive with exogenous inputs)method was used to study the coupling relationship model between the liquid level and related parameters under normal operating conditions.The model used historical liquid level values and related parameters as input regressors to predict the liquid level at the next time step.By comparing the predicted value with the actual observation,the magnitude of the residual could determine whether the current working state of the steam generator multi-sensor system was abnormal.Compared to traditional state monitoring methods that trigger alarms only after reaching preset liquid level thresholds,the results show that this method can detect deviations in the coupling relationship between the liquid level and related parameters and detect anomalies even with minor changes,thus enabling the monitoring and early warning of steam generator liquid levels.Validation with real nuclear power plant data demonstrates the model's ability to accurately regress liquid levels and achieve good anomaly detection performance in experiments using an abnormal dataset constructed based on real fault types.
steam generatorwater levelNARX(nonlinear auto-regressive with exogenous inputs)anomaly detection
周光荣、杨森权、郑胜、易爽
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中核武汉核电运行技术股份有限公司,武汉 430223
三峡大学理学院,宜昌 443002
中核集团核工业仿真技术重点实验室,武汉 430223
三峡大学电气与新能源学院,宜昌 443002
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蒸汽发生器 液位 带外源输入的非线性自回归(nonlinear auto-regressive with exogenous inputs,NARX) 异常检测
万方数据
维普
