TITLE:
Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems
AUTHORS:
Yousif Yahya, Ai Qian, Adel Yahya
KEYWORDS:
Dissolved Gas Analysis, Fault Diagnosis, Fuzzy Reasoning, Power Transformer Faults, Spiking Neural P System
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.8 No.4,
November
11,
2016
ABSTRACT: This paper presents an intelligent technique to fault diagnosis of power
transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking
neural P systems (FRSN P systems) as a membrane computing with distributed
parallel computing model is powerful and suitable graphical approach model in
fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers
faults identifications and capturing knowledge implicitly during the learning stage, using linguistic
variables, membership functions with “low”, “medium”, and “high” descriptions
for each gas signature, and inference rule base. Membership functions are used
to translate judgments into numerical expression by fuzzy numbers. The performance
method is analyzed in terms for four gas ratio (IEC 60599) signature as input
data of FRSN P systems. Test case results evaluate that the proposals method
for power transformer fault diagnosis can significantly improve the diagnosis
accuracy power transformer.