针对传统的基于数据驱动的故障诊断方法难以精准诊断核电厂反应堆冷却剂系统(reactor coolant system,RCS)故障这一问题,建立了一种核电厂RCS故障诊断模型.首先,应用基于交叉验证的递归特征消除算法(feature elimination with cross-validation,RFECV)选择模型的输入特征;然后,应用改进的鲸鱼优化算法(improved whale optimization algorithm,IWOA)优化XGBoost模型的超参数;最后,在上述基础上,应用XGBoost模型,建立RCS故障诊断模型.应用所建立的模型对冷却水丧失(loss of coolant accident,LOCA)、主泵卡轴(main pump trip,MPT)和蒸汽发生器管道破裂(steam generator tube rupture,SGTR)事故进行诊断,并将其与传统的故障诊断模型进行对比,验证了本文所建立模型的准确性.模型的诊断结果能够为保障核反应堆的安全稳定运行,杜绝核安全事故的发生提供重要参考.
Model for Fault Diagnosis of Reactor Coolant System for Nuclear Power Plants
Traditional data-driven fault diagnosis methods are hard to accurately diagnosis faults in the reactor coolant system(RCS)of a nuclear power plant.In order to address this challenge,a model for fault diagnosis of the RCS was established.Firstly,re-cursive feature elimination with cross-validation(RFECV)algorithm was used to select the features for the model.Then,improved whale optimization algorithm(IWOA)was used to tune the hyperparameters of the model.Finally,the XGBoost model was used to es-tablish the fault diagnosis model for RCS.The established model was applied to diagnose loss of coolant accident(LOCA),main pump trip(MPT),and steam generator tube rupture(SGTR),and the accuracy of the established model was validated by comparing with the traditional data-driven fault diagnosis models.The diagnosis result could be treated as a crucial reference for ensuring the safe and stable operation of the nuclear reactor and preventing nuclear accidents.
万方数据
维普
