English

Abstract

Power transformers has an important role in electrical power transmission and its interruption has financial losses, thus its condition monitoring is essential and performance of this equipment is effective for power system reliability. In this paper, proposed method has advantages of both probabilistic neural network (PNN) and Interval Type-2 Fuzzy Support Vector Machine (IT2FSVM). Firstly, main feature is extracted from primary and secondary three phase currents and search coils differential voltage by wavelet transform and this information is used as probabilistic neural network inputs. AI techniques are applied to establish classification features for faults in the transformers based on the collected gas data. The features are applied as input data to PNN and IT2FSVM combination of classifiers for faults classification. The experimental data from NTPC Korba-India is used to evaluate the performance of proposed method. The results of the various DGA methods are classified using AI techniques. In comparison to the results obtained from the AI techniques, the PNN plus IT2SVM has been shown to possess the most excellent performance in identifying the transformer fault type. The test results indicate that the PNN plus IT2SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification. In addition, the study aims to study the joint effect of PNN and IT2SVM on the classification performance when used together.