A Fault Diagnosis Method for Excitation Transformers Based on HPO-DBN and Multi-Source Heterogeneous Information Fusion

In response to the limitations of traditional single-signal approaches, which fail to comprehensively reflect fault conditions, and the difficulties of existing feature extraction methods in capturing subtle fault patterns in transformer fault diagnosis, this paper proposes an innovative fault diagnosis methodology. Initially, to address common severe faults in excitation transformers, Principal Component Analysis (PCA) is applied to reduce the dimensionality of multi-source feature data, effectively eliminating redundant information. Subsequently, to mitigate the impact of non-stationary noise interference in voiceprint signals, a Deep Belief Network (DBN) optimized using the Hunter–Prey Optimization (HPO) algorithm is employed to automatically extract deep features highly correlated with faults, thus enabling the detection of complex, subtle fault patterns. For temperature and electrical parameter signals, which contain abundant time-domain information, the Random Forest algorithm is utilized to evaluate and select the most relevant time-domain statistics. Nonlinear dimensionality reduction is then performed using an autoencoder to further reduce redundant features. Finally, a multi-classifier model based on Adaptive Boosting with Support Vector Machine (Adaboost-SVM) is constructed to fuse multi-source heterogeneous information. By incorporating a pseudo-label self-training strategy and integrating a working condition awareness mechanism, the model effectively analyzes feature distribution differences across varying operational conditions, selecting potential unseen condition samples for training. This approach enhances the model’s adaptability and stability, enabling real-time fault diagnosis. Experimental results demonstrate that the proposed method achieves an overall accuracy of 96.89% in excitation transformer fault diagnosis, outperforming traditional models such as SVM, Extreme Gradient Boosting with Support Vector Machine (XGBoost-SVM), and Convolutional Neural Network (CNN). The method proves to be highly practical and generalizable, significantly improving fault diagnosis accuracy.