Abstract This paper develops an automated fault detection tool to detect very small LOCAs in pressurized water reactors that would be difficult for operators to detect manually. One of the primary challenges with previous automated fault detection methods, which are data-driven, is that they require data from LOCAs; however, it may be difficult to capture real operational data from LOCA scenarios. This work uses a physics-inspired approach that equates the physical effects of a LOCA to changes in known variables. This approach enables the detection of very small LOCAs using data-driven approaches that use nominal operating data without the need for LOCA data. The approach combines data-driven modeling with control-theoretic estimation techniques to detect LOCAs and estimate their magnitudes in real-time. First, simulated process data for a variety of nominal operating conditions is collected using a generic pressurized water reactor simulator. Then, that data is used to train an artificial neural network regression model that captures the nonlinear plant dynamics. Finally, the regression model is used in a particle filter to detect the onset and estimate the magnitude of the leak. These methods are successfully verified using LOCA simulations that would be hard to manually distinguish from normal operating transients.

中文翻译：

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在没有事故特定数据的情况下检测冷却剂损失事故

摘要 本文开发了一种自动故障检测工具，用于检测压水反应堆中操作员难以手动检测的非常小的 LOCA。以前数据驱动的自动故障检测方法的主要挑战之一是它们需要来自 LOCA 的数据；然而，可能很难从 LOCA 场景中获取真实的操作数据。这项工作使用了一种受物理学启发的方法，该方法将 LOCA 的物理影响等同于已知变量的变化。这种方法可以使用数据驱动的方法检测非常小的 LOCA，这些方法使用标称运行数据，而无需 LOCA 数据。该方法将数据驱动建模与控制理论估计技术相结合，以检测 LOCA 并实时估计其幅度。第一的，使用通用压水反应堆模拟器收集各种标称操作条件的模拟过程数据。然后，该数据用于训练捕捉非线性设备动力学的人工神经网络回归模型。最后，在粒子滤波器中使用回归模型来检测泄漏的开始并估计泄漏的大小。这些方法使用 LOCA 模拟得到了成功验证，这些模拟很难与正常操作瞬态手动区分。