Diagnosis of Transformer Faults Based on Adaptive Neuro-Fuzzy Inference System
نویسندگان
چکیده
Transformer fault diagnosis is an interesting subject for plant operators due to its criticality in power systems. There are several international standards available to interpret power transformer faults based on dissolved gas analysis. In certain cases these standards are not able to provide correct diagnosis. There are several soft computing techniques available for modelling transformer faults. Adaptive Neuro-Fuzzy Inference System (ANFIS) modelling technique emerges as one of the soft computing modelling technique for power transformer. The objective of this paper is to obtain an ANFIS model from DGA system stimulus and response data of power transformers. The prediction ability of the ANFIS is also tested using limited data set for model training. Results show that ANFIS model is able to estimate the transformer faults with high level of accuracy.
منابع مشابه
Fault Diagnosis of Power Transformer Based on Dissolved Gas Analysis and Adaptive Neuro-fuzzy Inference System
Power Transformers are a vital link in a power system. Well-being of power transformer is very much important to the reliable operation of the power system. Dissolved Gas Analysis (DGA) is one for the effective tool for monitoring the condition of the transformer. To interpret the DGA result multiple techniques are available.IEC codes are developed to diagnose transformer faults. But there are ...
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