Z. J. Jin, J. T. Li and Z. S. Zhu, “Diagnosis of Transformer Winding Deformation on the Basis of Artificial Neural Network,” Proceedings of the 6th International Conference on Properties and Applications of Dielectric Materials, Xi’an Jiaotong University, Xi’an, 21-26 June 2000.

has been cited by the following article:

• TITLE: Detection of Mechanical Deformation in Old Aged Power Transformer Using Cross Correlation Co-Efficient Analysis Method

  AUTHORS: Asif Islam, Shahidul Islam Khan, Aminul Hoque

  KEYWORDS: Core Damage, Radial Deformation, Axial Deformation, Sweep Frequency Response Analysis, Cross Correlation Co-efficient, Power Transformer

  JOURNAL NAME: Energy and Power Engineering, Vol.3 No.4, September 27, 2011

  ABSTRACT: Detection of minor faults in power transformer active part is essential because minor faults may develop and lead to major faults and finally irretrievable damages occur. Sweep Frequency Response Analysis (SFRA) is an effective low-voltage, off-line diagnostic tool used for finding out any possible winding displacement or mechanical deterioration inside the Transformer, due to large electromechanical forces occurring from the fault currents or due to Transformer transportation and relocation. In this method, the frequency response of a transformer is taken both at manufacturing industry and concern site. Then both the response is compared to predict the fault taken place in active part. But in old aged transformers, the primary reference response is unavailable. So Cross Correlation Co-Efficient (CCF) measurement technique can be a vital process for fault detection in these transformers. In this paper, theoretical background of SFRA technique has been elaborated and through several case studies, the effectiveness of CCF parameter for fault detection has been represented.