A Prediction Model for Operator Diagnosis Level Integrating SACADA Database and Machine Learning in a Main Control Room of Nuclear Power Plants

Operator diagnosis level in a main control room (MCR) of Nuclear Power Plants (NPPs) is a core factor in preventing human errors and ensuring the safe operation of NPPs. Due to the high uncertainty of human behaviors and the scarcity of relevant data, traditional analysis methods mainly rely on empirical judgment, which suffer from insufficient dynamics and poor engineering adaptability. To address the issues, this paper conducts a study on an AI prediction model for operator diagnosis level in a MCR of NPPs based on the SACADA database and machine learning technology. The model adopts a probabilistic neural network (PNN) as the main architecture, and proposes a hybrid method of network search considering density distribution combined with K-fold cross-validation, which breaks the traditional mode of a single smoothing factor adapting to an entire dataset. The analysis results show that the performance of the hybrid method proposed in this paper outperforms network search + K-fold cross-validation and particle swarm optimization + K-fold cross-validation methods in terms of accuracy, precision, recall, and F1-score. The five-fold cross-validation verifies that the model has good stability and good generalization ability. Further, the model is compared with common AI models such as BP neural network and RBF neural network. The results demonstrate that the proposed model has advantages in core indicators including overall accuracy (0.9444), macro-precision (0.9783), macro-recall (0.9063), and macro-F1-score (0.9362), and can effectively solve the problems of insufficient recognition of minority-class samples, overfitting, and underfitting. This research achieves professional and in-depth application of the SACADA database for diagnosis level prediction, extends existing research on prediction tasks, and delivers valuable theoretical insights and practical application significance.