Classifying Transformer Winding Deformation Fault Types and Degrees Using FRA Based on Support Vector Machine

• Published in: IEEE access Vol. 7; pp. 112494 - 112504
• Main Authors: Liu, Jiangnan, Zhao, Zhongyong, Tang, Chao, Yao, Chenguo, Li, Chengxiang, Islam, Syed
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Circuit faults; Classification; Deformation effects; Diagnostic systems; Fault diagnosis; Feature extraction; FRA; Frequency analysis; Frequency response; Optimization; Power; Power transformers; Strain; Support vector machines; SVM; Transformer; Transformers; Winding; winding faults; Windings
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2932497

As an important part of power system, power transformer plays an irreplaceable role in the process of power transmission. Diagnosis of transformer's failure is of significance to maintain its safe and stable operation. Frequency response analysis (FRA) has been widely accepted as an effective tool for winding deformation fault diagnosis, which is one of the common failures for power transformers. However, there is no standard and reliable code for FRA interpretation as so far. In this paper, support vector machine (SVM) is combined with FRA to diagnose transformer faults. Furthermore, advanced optimization algorithms are also applied to improve the performance of models. A series of winding fault emulating experiments were carried out on an actual model transformer, the key features are extracted from measured FRA data, and the diagnostic model is trained and obtained, to arrive at an outcome for classifying the fault types and degrees of winding deformation faults with satisfactory accuracy. The diagnostic results indicate that this method has potential to be an intelligent, standardized, accurate and powerful tool.