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Accident diagnosis algorithm with untrained accident identification during power-increasing operation

机译：电源运行期间未训练事故识别的事故诊断算法

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摘要

To ensure the safety of nuclear power plants (NPPs) from accidents or anomalies, regulatory bodies provide procedures that describe safety regulations that must be followed. However, even if well-designed procedures are provided to operators, diagnostic activity in an emergency scenario is classified as an extremely demanding task. Moreover, the diagnosis of accidents occurring under various operation modes, such as power increasing, is expected to be extremely difficult, owing to the diverse behaviors and availability of systems and components. With regard to such emergency response issues, artificial neural network-based methods are regarded as one of the most promising approaches, because of their noticeable achievements. However, regarding the application of neural networks, in the case of an untrained accident, there is no capability to answer "do not know." This study aims to develop algorithms that can cover various NPP operation modes and deal with untrained accidents. To address the various NPP operation modes, the major changes that can affect the plant states are classified. Furthermore, to deal with untrained accidents, the applied diagnostic algorithms use long short-term memory and an autoencoder. Following this, this paper presents the implementation and test results of the accident diagnosis algorithms.
机译：为确保核电厂（NPPS）的事故或异常的安全性，监管机构提供描述必须遵循的安全法规的程序。然而，即使为运营商提供了良好设计的程序，即使提供了良好的程序，也将紧急情况下的诊断活动被归类为极其苛刻的任务。此外，由于系统和部件的不同行为和可用性，预期在各种操作模式下发生的事故发生的事故诊断，例如功率增加。关于此类应急响应问题，基于人工神经网络的方法被认为是最有前途的方法之一，因为他们显着的成就。然而，关于神经网络的应用，在未经训练的事故的情况下，没有能力回答“不知道”。本研究旨在开发可以涵盖各种NPP操作模式的算法，并处理未经训练的事故。为了解决各种NPP操作模式，可以对植物状态进行分类。此外，要处理未训练的事故，所应用的诊断算法使用长短短期内存和AutoEncoder。在此之后，本文提出了事故诊断算法的实现和测试结果。

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