International Journal of Renewable Energy Research-IJRER
Effect of Data Transformation on the Diagnostic Accuracy of Transformer Faults and the Performance of the Supervised Classifiers
Abstract
Dissolved gas analysis (DGA) is a common method used to diagnose transformer faults. The DGA methods such as IEC Code, Rogers' ratios, Duval triangle, and key gas methods failed to interpret the transformer faults in some cases and have poor diagnostic accuracy. Therefore, the researchers try to enhance the diagnostic accuracy by combining the traditional DGA techniques with artificial intelligence and optimization techniques. Still, they also have a complex way of interpreting the transformer faults. In the current work, a classification learner toolbox in MATLAB presented several Classifiers to classify the transformer faults and construct a classifier model used to diagnose some other test samples. The classification learner in MATLAB is so easy to understand and implement in classification application. Several data transformations were carried out to investigate their effect on diagnostic accuracy to identify which transformation method can achieve the highest diagnostic accuracy. The results indicated that the ensemble bagged classifier with raw data (data without any transformation) had the highest diagnostic accuracy of the transformer faults, reaching 83.4 %.
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DOI (PDF): https://doi.org/10.20508/ijrer.v12i2.12950.g8501
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