Utilizing MATLAB machine learning models to categorize transient events in a nuclear power plant using generic pressurized water reactor simulator

Abstract

Enhancing safety and dependability within nuclear power facilities holds paramount importance in safeguarding both individuals and the environment. The adoption of machine learning for diagnosing faults in these plants is steadily gaining interest, driven by its capacity to detect faults, alleviate human errors in high-pressure scenarios, and ensure the secure and consistent operation of these facilities swiftly and accurately.This paper examines the use of machine learning models for fault diagnostics, specifically, the identification of transient events in a nuclear power plant to reduce human errors. The data was collected from WSC’s Generic Pressurized Water Reactor (GPWR) simulator and processed using MATLAB. The simulator encompasses models for both the primary and secondary systems of the nuclear power plant (NPP). Additionally, it incorporates models for the control systems and instrumentation responsible for monitoring and regulating the reactor, serving as integral components for data extraction and transient modeling. A total of 9 different transient events were simulated with 12 different initial conditions to create a dataset with 72,000 observations. Nine types of classification models (33 total preset models) were trained and validated using the classification learners application. Among them, the Neural Network Classifiers (NNC) displayed the highest average accuracy of 90%. The Fine Tree, Ensemble Bagged Trees, and Medium Neural Network models were the best-performing individual models with validation accuracies above 90% and a maximum training time of 8 min. These models were further analyzed using accuracy, confusion matrix, precision, recall, and F1 score. To optimize these models, techniques such as different validation schemes and feature selection were utilized to further reduce their training time and improve their prediction accuracy. The optimized models boasted comparable accuracies with a maximum training time of under 1.5 min. The results of this study exhibited favorable comparisons with other machine-learning endeavors in the field of reactor transient detection and diagnostics. Notably, the study maintained low execution and computation times while preserving high levels of accuracy. This study offers insightful information on how AI and machine learning can be used to improve nuclear power plant diagnostics, enhance safety, and provide support to the operator.