29 / 2024-05-31 18:55:25

Comparative Study of Imbalanced Sample Handling Methods in Nuclear Power Plants

Generative Adversarial Networks; Copula Entropy; Imbalanced Sample Handling; Nuclear Power Plants

Imbalanced sample distribution, with an abundance of normal samples and a scarcity of fault samples, along with uneven distribution among different types of faults, poses a challenge in analyzing operational data from nuclear power plants. Various methods have been proposed in current research to address this issue, including generative adversarial networks (GAN), under-sampling and over-sampling, and ensemble learning techniques. However, there lacks targeted research on the severity of imbalanced samples' impact on diagnostic models for nuclear power plants and a comprehensive performance comparison of various typical methods for handling imbalanced samples. This study focuses on typical approaches such as GAN, SMOTE over-sampling, and SMOTE-Boost ensemble learning, conducting simulations to assess the effects of imbalanced data, evaluate the performance differences, advantages, and disadvantages of these methods. Additionally, it proposes a novel imbalanced sample diagnostic method, CE-GAN-RF, incorporating Copula Entropy (CE) feature extraction module, GAN generation model, and random forest (RF) classification model, to offer new insights into imbalanced sample diagnosis techniques for nuclear power plants.