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Machine learning based fault prediction system for the primary heat transport system of CANDU type pressurized heavy water reactor

机译:基于机器学习的CAMLU型加压重水反应器主要传热系统故障预测系统

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In nuclear power reactor, temperature of the core has to be maintained within the safety limits. This can be achieved by monitoring and controlling the various system's parameter of nuclear reactor. One of the important system of nuclear reactor is primary heat transfer (PHT) system. Therefore, any fault in the PHT system may lead to the state where PHT parameters cross the safety limits, and reactor becomes unsafe for operation. To avoid such conditions various fault monitoring and controlling systems have been used in nuclear power reactors. In the recent years, machine learning techniques have been used to build automatic fault prediction system which can be used as a fault monitoring system of nuclear power plant. In this paper, we propose our approach to build machine learning based fault prediction system for the PHT system of CANDU (Canada Deuterium Uranium) type reactor. The proposed approach is based on the classification techniques of supervised machine learning. Whereas, to validate our approach, we performed an experiment by extracting the historical data of the following reactor's parameters: coolant flow rate, coolant header temperature, and neutron power rate. After extracting the parameter's data, we labeled the data with the following three plant statuses: running, transient and shutdown. Finally, we used the binary tree and artificial neural network techniques of machine learning and built models which successfully classified the three statuses of the plant. In our experiment, the maximum obtained accuracy of our model is 99%. It shows that our proposed system can be used to predict fault in PHT loop.
机译:在核动力反应堆中,芯的温度必须在安全限度内维持。这可以通过监视和控制核反应堆的各种系统的参数来实现。一个核反应堆的重要系统的是主传热(PHT)系统。因此,在PHT系统的任何故障可能导致其中PHT参数越过安全限制的状态下,和反应器变得不安全的操作。为了避免这种情况的各种故障监测和控制系统在核电反应堆中使用。近年来,机器学习技术已被用于建立可用于核电厂的故障监控系统故障自动预测系统。在本文中,我们提出我们的构建机器为基础的学习故障预报系统方法的CANDU(加拿大氘铀)的反应装置中的PHT系统。所提出的方法是基于监督的机器学习的分类技术。冷却剂的流速,冷却剂集管温度,和中子功率率:而,以验证我们的方法中,我们通过提取以下反应器的参数的历史数据进行的实验。提取参数的数据后,我们有以下三种植物状态标记的数据:跑步,瞬态和关机。最后,我们使用二叉树和机器学习的人工神经网络技术和成功地归类工厂的三个状态建立的模型。在我们的实验中,我们的模型获得的最大精度为99%。这表明,我们提出的系统可用于PHT循环预测故障。

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