Optimizing Transformer Fault Detection Performance Through the Synergy of AI and Statistical Analysis for Multi-Fault Classification

The recent development of artificial intelligence (AI) has opened new avenues in processing parts per million (ppm) for fault detection through dissolved gas analysis (DGA). According to the latest IEC and IEEE standards, the existing methods are only applicable on single fault occurrence. The paper focuses on the challenge of detecting multiple faults occurring simultaneously in cases involving many faults using AI. Further, an inadequate training sample for classification and unavailability of balanced per-fault data reduces the model generalization, increases the risk of overfitting and biased learning towards the majority class. The proposed approach involves normalizing raw ppm values using z-score normalization, reducing dimensionality through t-distributed stochastic neighbor embedding (t-SNE), and synthesizing data using a generative adversarial network (GAN). Additionally, the parameters of error-correcting output codes (ECOC) and forest classifiers are optimized using a genetic algorithm (GA), efficiently solving multiple faults. F1 score, area under curve (AUC), and k-fold loss are used to evaluate fitness for improved classifier performance. This method outperforms the Duval method, and the data synthesis represents a new contribution to the field. The proposed method can achieve an overall accuracy of 99.6%, 98.6%, and 97.3% for the 9, 15, and 31 classes, respectively.