چکیده مقاله
Partial Discharge PD is one of the best methods for condition monitoring of transformers In this paper, six types of model transformers, instead of artificial defect models, are designed and manufactured to implant five different defects scratch on winding insulation, bubble in oil, moisture in insulation paper, very small free metal particle in transformer tank, fixed sharp metal point on transformer tank on each model, separately The Continuous Wavelet Transform CWT applied to each related measured time domain PD signal, results in an image representing each single PD signal in time frequency domain Then, the Gray Level Covariance Matrix GLCM is constructed based on the images from the CWT of PD signals The texture features are extracted from the constructed GLCM of each PD signal Also, the principal component analysis are applied to decrease the feature spaces and six first principal components are considered as inputs of the support vector machine for classifying each type of defect models Results indicate the efficiency of the proposed methods with accurate distinguishing type of defects
کلیدواژهها
نویسندگان
Vahid Parvin Darabad
Golestan University, Gorgan, Iran
شیوه ارجاع
Parvin Darabad, Vahid,1396,Applicationof Partial Discharge Pattern Recognition on Small Scale DefectedTransformers,Fifth International Conference on Electrical and Computer Engineering with Emphasis on Indigenous Knowledge,Tehran
ارائهشده در
مجموعه مقالات پنجمین کنفرانس بین المللی مهندسی برق و کامپیوتر با تاکید بر دانش بومی19 بهمن 1396 · تهران