标题：Artificial Intelligence Based Fault Diagnosis of Power Transformer-A Probabilistic Neural Network and Interval Type-2 Support Vector Machine Approach
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2015
卷号：4
期号：1
页码：18846
DOI：10.15680/IJIRSET.2015.0401031
出版社：S&S Publications
摘要：Power transformers has an important role in electrical power transmission and its interruption hasfinancial losses, thus its condition monitoring is essential and performance of this equipment is effective for powersystem reliability. In this paper, proposed method has advantages of both probabilistic neural network (PNN) andInterval Type-2 Fuzzy Support Vector Machine (IT2FSVM). Firstly, main feature is extracted from primary andsecondary three phase currents and search coils differential voltage by wavelet transform and this information is used asprobabilistic neural network inputs. AI techniques are applied to establish classification features for faults in thetransformers based on the collected gas data. The features are applied as input data to PNN and IT2FSVM combinationof classifiers for faults classification. The experimental data from NTPC Korba-India is used to evaluate theperformance of proposed method. The results of the various DGA methods are classified using AI techniques. Incomparison to the results obtained from the AI techniques, the PNN plus IT2SVM has been shown to possess the mostexcellent performance in identifying the transformer fault type. The test results indicate that the PNN plus IT2SVMapproach can significantly improve the diagnosis accuracies for power transformer fault classification. In addition, thestudy aims to study the joint effect of PNN and IT2SVM on the classification performance when used together.
关键词：Probabilistic Neural Network (PNN); Interval Type-2 Fuzzy Logic; Support Vector Machines;Dissolved gas analysis; Transformer Fault Diagnosis
