Dissolved gas analysis (DGA) is widely used for oil-immersed power transformers as a conventional fault diagnosis tool. However, interpretation criteria from DGA assessment often depends on empirical discrimination from a specialist, which can render unreliable or ambiguous diagnoses. Intelligent fault classification algorithms can be implemented to conquer uncertainty in conventional methods, and which require feature learning of transformer condition information data rather than expert experience. In this paper, a Gaussian process multi-classification (GPMC) method is proposed, which uses multiclass recognition with a Gaussian process (GP) and renders an output with a probabilistic interpretation rather than a deterministic guess. The method is investigated using largescale DGA field datasets to improve diagnostic accuracy, and presents reliable incipient fault diagnosis ability. A kernel-based learning algorithm and versatile artificial intelligence (AI) methods, (support vector machine (SVM), artificial neural network (ANN), K-nearest neighbors (KNN), decision tree and logistic regression (LR)) have been used to obtain comparative classification accuracy in comparison to the proposed method: additional comparison is demonstrated between conventional DGA and AI methods. The effectiveness and robustness of the proposed GPMC method are confirmed by experimental accuracy >95%, which illustrates that the proposed method is able to provide superior and reliable diagnoses for operational transformer faults.

Artificial intelligence (AI) methods have been used widely in power transformer fault diagnosis with notable developments in solutions for big data problems. Training data is essential to accurately train AI models. The volume, scope and variety of data samples contribute significantly to the success and reliability of diagnostic outcomes. This paper provides a comprehensive review and comparison of different augmentation methods used to generate reliable data samples for minority and majority classes to balance the diversity and distribution of dissolved gas analysis (DGA) datasets. The augmentation method presented in this paper combines three common AI models—the Support Vector Machine (SVM), Decision Tree, and k‐Nearest Neighbour (KNN)—to assess performance for diagnostic fault determination and classification, with comparator assessment using no data augmentation. Comparative analysis of the hybrid models uses evaluation metrics including accuracy, precision, recall, specificity, F‐score, G‐mean, and the area under receiver operation characteristic (Auc). Experimental results presented in this paper confirm that the data augmentation applied to AI models can resolve difficulties in imbalanced data distribution and provide significant improvements for fault diagnosis, particularly for minority classes.

No abstract

No abstract