Transformer Fault Diagnosis Based on Parallel AdaBoost-NB Algorithm on Spark Cloud Platform - Liu, Cheng; Ji, Lin (International journal of grid and high performance computing, 2025)
Power transformers are crucial equipment in the power grid because they are essential for ensuring stable grid operation. Sequential machine learning and artificial intelligence diagnostic algorithms often face issues of low efficiency and prolonged processing times with respect to handling large volumes of oil-immersed transformer fault data. In this article, the authors propose a new transformer fault diagnosis method that is based on the parallel AdaBoost-Naive Bayes algorithm. This method allows for resampling and reweighting, making the model pay more attention to samples that are difficult to classify and thereby improving performance on imbalanced datasets. The Spark platform is used for parallel processing of massive data, utilizing the cluster's multiple nodes for efficient fault diagnosis. Experimental results show that compared with traditional diagnostic methods, the proposed method achieves a significant improvement in diagnostic accuracy, with an accuracy rate of 93.38%. The significant speedup ratio achieved by parallel processing technology underscores its effectiveness and advantages in handling large-scale transformer fault data.