@article{MAKHILLIJSC20149521223,
    title = {Application of Adaptive Neuro-Fuzzy Inference System Based on IEC Method for Transformer Fault Diagnosis},
    journal = {International Journal of Soft Computing},
    volume = {9},
    number = {5},
    pages = {333-337},
    year = {2014},
    issn = {1816-9503},
    doi = {ijscomp.2014.333.337},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2014.333.337},
    author = {A.,P. and},
    keywords = {Dissolved gas analysis,IEC TC 10 database,artificial intelligence,transformer fault diagnosis,chromatograph},
    abstract = {Power transformer is one of the most important components 
  in a power system. It experiences thermal and electrical stresses during its 
  operation. The insulation system consisting of mineral oil and the insulation 
  paper used in transformer undergoes chemical changes under these stresses and 
  gases are generated. These gases dissolve in oil. The dissolved gases are extracted 
  in the laboratory using gas chromatograph. The dissolved gases are used for 
  fault identification. The fault identifications in a transformer are based on 
  certain key-gas ratios. International standards such as IEEE and ASTM are used 
  for fault identification. However, these standards are not able to diagnose 
  the faults under certain conditions. Hence, there is a need to improve the diagnostic 
  accuracy. This study attempts to diagnose the faults in a power transformer 
  using adaptive Neuro-Fuzzy Inference System. Simulation model is developed using 
  MATLAB<SUP>TM</SUP> Software and trained using the IEC TC 10 database of faulty 
  equipments inspected in service. The outputs of the adaptive Neuro-Fuzzy Inference 
  System based model are compared with the Roger&#146;s 
  Ratio Method. The comparison shows that the condition assessments offered by 
  the adaptive Neuro-Fuzzy Inference System based model is capable of predicting 
  the transformer faults with higher accuracy.}
    }