Detecting transformer faults is critical to avoid the undesirable loss of transformers from
service and ensure utility service continuity. Transformer faults diagnosis can be determined based
on dissolved gas analysis (DGA). The DGA traditional techniques, such as Duval triangle, Key
gas, Rogers’ ratio, Dornenburg, and IEC code 60599, suffer from poor transformer faults diagnosis.
Therefore, recent research has been developed to diagnose transformer fault and the diagnostic
accuracy using combined traditional methods of DGA with artificial intelligence and optimization
methods. This paper used a novel meta-heuristic technique, based on Gravitational Search and Dipper
Throated Optimization Algorithms (GSDTO), to enhance the transformer faults’ diagnostic accuracy,
which was considered a novelty in this work to reduce the misinterpretation of the transformer faults.
The robustness of the constructed GSDTO-based model was addressed by the statistical study using
Wilcoxon’s rank-sum and ANOVA tests. The results revealed that the constructed model enhanced
the diagnostic accuracy up to 98.26% for all test cases.