In abnormal states of nuclear power plants (NPPs), operators undertake mitigation actions to restore a normal state and prevent reactor trips. However, in abnormal states, the NPP condition fluctuates rapidly, which can lead to human error. If human error occurs, the condition of an NPP can deteriorate, leading to reactor trips. Sudden shutdowns, such as reactor trips, can result in the failure of numerous NPP facilities and economic losses. This study develops a remaining trip time (RTT) prediction system as part of an operator support system to reduce possible human errors and improve the safety of NPPs. The RTT prediction system consists of an algorithm that utilizes artificial intelligence (AI) and explainable AI (XAI) methods, such as autoencoders, light gradient-boosting machines, and Shapley additive explanations. AI methods provide diagnostic information about the abnormal states that occur and predict the remaining time until a reactor trip occurs. The XAI method improves the reliability of AI by providing a rationale for RTT prediction results and information on the main variables of the status of NPPs. The RTT prediction system includes an interface that can effectively provide the results of the system.

中文翻译：

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基于人工智能的核电站剩余跳闸时间预测系统的开发

在核电站（NPP）出现异常状态时，运营商采取缓解措施以恢复正常状态并防止反应堆跳闸。然而，在异常状态下，NPP状况快速波动，可能导致人为错误。如果发生人为错误，核电站的状况可能会恶化，导致反应堆跳闸。突然关闭（例如反应堆跳闸）可能会导致众多核电厂设施发生故障并造成经济损失。本研究开发了剩余行程时间（RTT）预测系统，作为操作员支持系统的一部分，以减少可能的人为错误并提高核电站的安全性。 RTT 预测系统由利用人工智能 (AI) 和可解释人工智能 (XAI) 方法的算法组成，例如自动编码器、光梯度增强机和 Shapley 附加解释。人工智能方法提供有关发生的异常状态的诊断信息，并预测反应堆跳闸发生之前的剩余时间。 XAI方法通过提供RTT预测结果的基本原理和核电厂状态主要变量的信息，提高了AI的可靠性。 RTT预测系统包括一个可以有效提供系统结果的接口。