Sahri, Z.B. and Yusof, R.B. (2014) Support Vector Machine-Based Fault Diagnosis of Power Transformer Using k Nearest-Neighbor Imputed DGA Dataset. Journal of Computer and Communications, 2, 22-31.
http://dx.doi.org/10.4236/jcc.2014.29004

has been cited by the following article:

• TITLE: A New Approach with Three Dimension Figure and ANSI/IEEE C57.104 Standard Rule Diagnoses Transformer’s Insulating Oil

  AUTHORS: Ming-Jong Lin

  KEYWORDS: Power Transformer Diagnosis, Dissolved Gas Analysis, Total Combustible Gases, Distribution Substation (D/S)

  JOURNAL NAME: Engineering, Vol.6 No.12, November 28, 2014

  ABSTRACT: The dissolved gas analysis (DGA) is an effective method for detecting incipient faults in immersed oil power transformers. In this paper, we investigate the DGA methods and employ the ANSI/IEEE C57.104 standards (guidelines for the interpretation of gases generated in oil-immersed transformers) and IEC Basic Gas Ratio method to design a heuristic power transformer fault diagnosis tool in practice. The proposed tool is implemented by a MATLAB program and it can provide users a transformer diagnosis result. The user keys in the data of H2, CH4, C2H2, C2H4, and C2H6 gases dissolved from the immersed oil transformer’s insulating oil measured by ASTM D3612. The analyzed results will be represented in texts and figures. The real measured data of the transformer oil were taken from Taiwan Power Company substations to verify the validation and accuracy of the developed diagnosis tool.