A novel method for power transformer fault diagnosis considering imbalanced data samples - Jun Chen; Yong Wang; Lingming Kong; Yilong Chen; Mianzhi Chen; Qian Cai; Gehao Sheng (Frontiers in Energy Research)
IntroductionMachine learning-based power transformer fault diagnosis methods often grapple with the challenge of imbalanced fault case distributions across different categories, potentially degrading diagnostic accuracy. To address this issue and enhance the accuracy and operational efficiency of power transformer fault diagnosis models, this paper presents a novel fault diagnosis model that integrates Neighborhood Component Analysis (NCA) and k-Nearest Neighbor (KNN) learning, with the incorporation of correction factors.MethodsThe methodology begins by introducing a correction factor into the objective function of the NCA algorithm to reduce the impact of sample imbalance on model training. We derive a sample parameter correlation quantization matrix from oil chromatography fault data using association rules, which serves as the initial value for the NCA algorithm’s training metric matrix. The metric matrix obtained from training is then applied to perform a mapping transformation on the input data for the KNN classifier, thereby reducing the distance between similar samples and enhancing KNN classification performance. Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy.ResultsAnalysis of the transformer fault case library reveals that the model proposed in this paper reduces diagnostic time by nearly half compared to traditional machine learning diagnosis models. Additionally, the accuracy for minority sample classes is improved by at least 15% compared to other models.DiscussionThe integration of NCA and KNN with correction factors not only mitigates the effects of sample imbalance but also significantly enhances the operational efficiency and diagnostic accuracy of power transformer fault diagnosis. The proposed model’s performance improvements highlight the potential of this approach for practical applications in the field of power transformer maintenance and diagnostics.